Analysis Of Temporal Dynamics Research Articles

AutoGRN: An adaptive multi-channel graph recurrent joint optimization network with Copula-based dependency modeling for spatio-temporal fusion in electrical power systems

Multi-sensor, multi-source information fusion presents significant challenges in complex real-world applications such as power consumption prediction, where existing methods often have limitations in capturing both spatio-temporal features and fully exploit complex relationships among multi-variate features simultaneously. In real-world scenarios, such as complex electrical power system settings, capturing both correlations is important, as spatio-temporal contains vital geographical information and complex inter-series relationships between features. To address these limitations, we propose AutoGRN for enhancing prediction accuracy and efficiency in multi-source spatio-temporal data fusion, with a focus on complex electrical power system settings. AutoGRN integrates a novel adaptive multi-channel attentive framework with copula-based dependency modeling, combining graph neural diffusion convolution and recurrent optimization. The framework automatically learns spatial features, capturing complex correlations among regions, while a sequence encoder extracts temporal patterns, ensuring the acquisition of time series characteristics such as seasonality and trends. High-dimensional spatio-temporal features are then fused through a specially designed multi-channel recurrent graph neural network, incorporating copula functions to model complex dependencies between variables. Extensive experiments on multiple real-world electricity consumption datasets demonstrate that AutoGRN achieves substantial advantages over state-of-the-art benchmarks in multi-variate prediction tasks, showcasing its potential for applications in various multi-sensor, multi-source fusion scenarios, particularly in complex systems requiring simultaneous analysis of spatial and temporal dynamics with intricate inter-variable dependencies. Code is available at https://github.com/AmbitYuki/AutoGRN.

Multi-output discrete grey model tailored for electricity consumption forecast

To address the limitation of many conventional grey forecasting methods that prioritize temporal dynamics analysis while often overlooking the essential spatial characteristics, we developed a multi-output discrete grey model tailored for forecasting electricity consumption in China's Yangtze River Delta, incorporating spatial effects. Specifically, we design a dynamic spatial interaction matrix to precisely capture the spatial correlations among neighboring areas, and integrate multiple sets of fixed-effect parameters to adeptly address spatial heterogeneity. Additionally, we implement regularization technique to prevent overfitting and utilize the Bayesian Optimization algorithm for hyper-parameter adjustment, considerably enhancing the model's stability of parameter estimation and its resilience to noise. Finally, a comprehensive case study, benchmarked against seven other models, highlights our model's outstanding multi-step predictive accuracy and robustness against input data uncertainty, exceeding the performance of existing methods.

A Statistical Framework for Analysis of Trial-Level Temporal Dynamics in Fiber Photometry Experiments.

Fiber photometry has become a popular technique to measure neural activity in vivo, but common analysis strategies can reduce detection of effects because they condense within-trial signals into summary measures, and discard trial-level information by averaging across-trials . We propose a novel photometry statistical framework based on functional linear mixed modeling, which enables hypothesis testing of variable effects at every trial time-point , and uses trial-level signals without averaging. This makes it possible to compare the timing and magnitude of signals across conditions while accounting for between-animal differences. Our framework produces a series of plots that illustrate covariate effect estimates and statistical significance at each trial time-point. By exploiting signal autocorrelation, our methodology yields joint 95% confidence intervals that account for inspecting effects across the entire trial and improve the detection of event-related signal changes over common multiple comparisons correction strategies. We reanalyze data from a recent study proposing a theory for the role of mesolimbic dopamine in reward learning, and show the capability of our framework to reveal significant effects obscured by standard analysis approaches. For example, our method identifies two dopamine components with distinct temporal dynamics in response to reward delivery. In simulation experiments, our methodology yields improved statistical power over common analysis approaches. Finally, we provide an open-source package and analysis guide for applying our framework.

Dynamic in situ detection in iRhizo-Chip reveals diurnal fluctuations of Bacillus subtilis in the rhizosphere

Effective colonization by microbe in the rhizosphere is critical for establishing a beneficial symbiotic relationship with the host plant. Bacillus subtilis, a soil-dwelling bacterium that is commonly found in association with plants and their rhizosphere, has garnered interest for its potential to enhance plant growth, suppress pathogens, and contribute to sustainable agricultural practices. However, research on the dynamic distribution of B. subtilis within the rhizosphere and its interaction mechanisms with plant roots remains insufficient due to limitations in existing in situ detection methodologies. To achieve dynamic in situ detection of the rhizosphere environment, we established iRhizo-Chip, a microfluidics-based platform. Using this device to investigate microbial behavior within the rhizosphere, we found obvious diurnal fluctuations in the growth of B. subtilis in the rhizosphere. Temporal dynamic analysis of rhizosphere dissolved oxygen (DO), pH, dissolved organic carbon, and reactive oxygen species showed that diurnal fluctuations in the growth of B. subtilis are potentially related to a variety of environmental factors. Spatial dynamic analysis also showed that the spatial distribution changes of B. subtilis and DO and pH were similar. Subsequently, through in vitro control experiments, we proved that rhizosphere DO and pH are the main driving forces for diurnal fluctuations in the growth of B. subtilis. Our results show that the growth of B. subtilis is driven by rhizosphere DO and pH, resulting in diurnal fluctuations, and iRhizo-Chip is a valuable tool for studying plant rhizosphere dynamics.

Decision-Making with Predictions of Others' Likely and Unlikely Choices in the Human Brain.

For better decisions in social interactions, humans often must understand the thinking of others and predict their actions. Since such predictions are uncertain, multiple predictions may be necessary for better decision-making. However, the neural processes and computations underlying such social decision-making remain unclear. We investigated this issue by developing a behavioral paradigm and performing functional magnetic resonance imaging and computational modeling. In our task, female and male participants were required to predict others' choices in order to make their own value-based decisions, as the outcome depended on others' choices. Results showed, to make choices, the participants mostly relied on a value difference (primary) generated from the case where others would make a likely choice, but sometimes they additionally used another value difference (secondary) from the opposite case where others make an unlikely choice. We found that the activations in the posterior cingulate cortex (PCC) correlated with the primary difference while the activations in the right dorsolateral prefrontal cortex (rdlPFC) correlated with the secondary difference. Analysis of neural coupling and temporal dynamics suggested a three-step processing network, beginning with the left amygdala signals for predictions of others' choices. Modulated by these signals, the PCC and rdlPFC reflect the respective value differences for self-decisions. Finally, the medial prefrontal cortex integrated these decision signals for a final decision. Our findings elucidate the neural process of constructing value-based decisions by predicting others and illuminate their key variables with social modulations, providing insight into the differential functional roles of these brain regions in this process.

Modeling and analysis of temporal dynamics in groundwater aquifers of New Valley Oases, Egypt

Water scarcity poses a significant challenge in arid and semi-arid regions, necessitating a focused exploration of groundwater resources. Egypt, confronted with various water challenges, particularly in its Western Desert, relies heavily on groundwater as the exclusive water source due to the presence of the Nubian Sandstone aquifer. Effective groundwater management in this region is imperative. This study delves into the hydrogeological characteristics of the Nubian Sandstone aquifer system (NSAS) in the prominent New Valley Oases—Kharga, Dakhla, and Farafra—where agricultural activities heavily depend on groundwater. The primary objective entails a meticulous temporal assessment of the impact of groundwater development on aquifer behavior, groundwater levels, and drawdown. Employing a remote sensing approach, agricultural expansion from 1995 to 2020 was scrutinized. The Visual MODFLOW package served as a robust tool for simulating groundwater flow in the study areas. Noteworthy findings reveal an upward trajectory in agricultural crop areas, escalating by approximately 6% from 1740 km² in 1995 to 1850 km² in 2020. Concurrently, drawdown, influenced by current groundwater extraction, is anticipated to range from 0.5 to 5 meters per year. To ensure the sustainable development of these areas, stringent regulations must be implemented, underscoring the imperative for judicious groundwater management practices. This research underscores the critical need for informed decision-making and proactive measures to address the evolving dynamics of groundwater resources in the New Valley Oases.

Analysis of temporal and spatial dynamics and driving factors in the aquaculture industry of Fuding City, China

BackgroundThe sustainable development of aquaculture is a crucial determinant of food security, the well-being of aquaculture practitioners, and economic growth within coastal regions. Considering the existing gap in research regarding spatial and temporal development of aquaculture, this study investigates the progression of aquaculture practices over time and across various locations in Fuding City, China. MethodsWe retrospectively collected temporal and spatial data on aquaculture, as well as demographic, social, and economic data for Fuding City from 2010 to 2020. By employing 3D kernel density analysis, we illustrated the temporal and spatial changes in aquaculture. Furthermore, we utilized Ordinary Least Squares regression to investigate the driving factors behind the spatial changes in the aquaculture industry. ResultsOver the past decades, we observed that in Fuding City, both the number of fishing rafts and aquaculture households initially decreased and then increased. The spatial distribution of aquaculture experienced a shift from the west (inner bay area) to the east (coastal area). Additionally, the type of fishing rafts also varied by region, with traditional rafts dominating the western inner bay and plastic rafts prevalent in the eastern offshore areas. Analysis of driving factors revealed that at least six factors have a significant positive correlation with the eastward shift of the aquaculture industry's center, including the proportion of migrant population, proportion of aquaculture to total fishery output, average temperature, investment in aquaculture technology, total fish sales, and GDP of Fuding City. ConclusionThis study examines the spatial and temporal dynamics of aquaculture in Fuding City from 2010 to 2020, proposing an innovative approach to spatial optimization that integrates both horizontal and vertical strategies. These insights aim to guide the development of coastal aquaculture policies and support sustainable regional development, fostering a balanced coexistence between human activities and marine environments.

Sex differences in age-varying trends of depressive symptoms, substance use, and their associations among South Korean adults: A Time-Varying Effect Modeling (TVEM) analysis of a nationwide sample

BackgroundThis study investigated sex differences in the age-varying trends of depressive symptoms, substance use, and their relationships throughout the adult lifespan. Using a nationwide sample from South Korea, this study aimed to confirm existing patterns and identify unique characteristics specific to the South Korean context. MethodTime-Varying Effect Modeling (TVEM) was applied to data from 17,484 participants (9987 women and 7497 men) in the Korea National Health and Nutrition Examination Survey. ResultsConsistent with global trends, the results revealed a higher prevalence of depressive symptoms among women and a greater prevalence of substance use among men. However, the findings also illuminated unique patterns within the South Korean context. Substance use among South Korean men peaked during their 40s, whereas South Korean women consistently exhibited lower rates of substance use. Additionally, a stronger association between depressive symptoms and substance use was identified in women compared to men. LimitationsThe study used cross-sectional data, limiting the analysis of temporal dynamics between depressive symptoms and substance use. Additionally, TVEM cannot distinguish between aging and cohort effects. Furthermore, the assessments of depressive symptoms and substance use were based on self-report. Finally, the study did not include adolescents or sex and gender minorities in its sample. ConclusionsThese results emphasize the need for targeted interventions, particularly among women in their 20s and older adulthood, where there is a heightened co-occurrence of depressive symptoms and substance use. These findings also highlight the importance of sex- and culture-sensitive approaches tailored to the South Korean context.

Aberrant patterns of spontaneous brain activity in schizophrenia: A resting-state fMRI study and classification analysis

BackgroundSchizophrenia is a prevalent mental disorder, leading to severe disability. Currently, the absence of objective biomarkers hinders effective diagnosis. This study was conducted to explore the aberrant spontaneous brain activity and investigate the potential of abnormal brain indices as diagnostic biomarkers employing machine learning methods. MethodsA total of sixty-one schizophrenia patients and seventy demographically matched healthy controls were enrolled in this study. The static indices of resting-state functional magnetic resonance imaging (rs-fMRI) including amplitude of low frequency fluctuations (ALFF), fractional ALFF (fALFF), regional homogeneity (ReHo), and degree centrality (DC) were calculated to evaluate spontaneous brain activity. Subsequently, a sliding-window method was then used to conduct temporal dynamic analysis. The comparison of static and dynamic rs-fMRI indices between the patient and control groups was conducted using a two-sample t-test. Finally, the machine learning analysis was applied to estimate the diagnostic value of abnormal indices of brain activity. ResultsSchizophrenia patients exhibited a significant increase ALFF value in inferior frontal gyrus, alongside significant decreases in fALFF values observed in left postcentral gyrus and right cerebellum posterior lobe. Pervasive aberrations in ReHo indices were observed among schizophrenia patients, particularly in frontal lobe and cerebellum. A noteworthy reduction in voxel-wise concordance of dynamic indices was observed across gray matter regions encompassing the bilateral frontal, parietal, occipital, temporal, and insular cortices. The classification analysis achieved the highest values for area under curve at 0.87 and accuracy at 81.28% when applying linear support vector machine and leveraging a combination of abnormal static and dynamic indices in the specified brain regions as features. ConclusionsThe static and dynamic indices of brain activity exhibited as potential neuroimaging biomarkers for the diagnosis of schizophrenia.

Analysis of Spatial and Temporal Dynamics of Finland’s Boreal Forests and Types over the Past Four Decades

In the context of global warming, the study of the long-term spatial change characteristics of boreal forest cover is not only important for global climate change and sustainable development research but can also provide support for further research on the response of boreal forest changes to climate change. Using Landsat TM/OLI images from 1980 to 2020 as the data source and Google Earth Engine (GEE) as the platform, Finland was selected as the study area of boreal forests, and typical sample points of different features were chosen to classify forested and non-forested land using the random forest algorithm combined with spectral indices and classified feature sets of tasseled cap transform to obtain the four-phase forest cover change maps of the region. GEE test sample points and random selection points of images from the GF-2 and GF-7 satellites were used for verification. The classification accuracy was 97.17% and 88.9%. The five-phase forest cover images were segmented by a 2° latitude zone, and the spatial and temporal dynamic changes in forest cover in the whole area and each latitude zone were quantified by pixel superposition analysis. The results showed that, in the past 40 years, the boreal forest cover in Finland changed significantly, and the forest cover decreased from 75.79% to 65.36%, by 10.43%. Forest change mainly occurs in coniferous forests, whereas broadleaf forests are more stable. The forest coverage in each latitude zone decreased to varying degrees, with higher changes occurring in high-latitude areas above 64° N between 1980 and 2000, and higher and more severe changes occurring in low-latitude areas below 64° N between 2000 and 2020. Coniferous forests are the dominant type of forest in Finland, and the degradation of coniferous forests in the south is likely to become more severe, whereas the north and above is likely to become more favorable for coniferous forests. More monitoring and research are needed to follow up on the very different changes in the north and south regions.

Visual cortical area contributions to the transient, multifocal and steady-state VEP: A forward model-informed analysis

Abstract Central to our understanding of how visual-evoked potentials (VEPs) contribute to visual processing is the question of where their anatomical sources are. Three well-established measures of low-level visual cortical activity are widely used: the first component (“C1”) of the transient and multifocal VEP, and the steady-state VEP (SSVEP). Although primary visual cortex (V1) activity has often been implicated in the generation of all three signals, their dominant sources remain uncertain due to the limited resolution and methodological heterogeneity of source modelling. Here, we provide the first characterisation of all three signals in one analytic framework centred on the “cruciform model”, which describes how scalp topographies of V1 activity vary with stimulus location due to the retinotopy and unique folding pattern of V1. We measured the transient C1, multifocal C1, and SSVEPs driven by an 18.75 Hz and 7.5 Hz flicker, and regressed them against forward models of areas V1, V2, and V3 generated from the Benson-2014 retinotopy atlas. The topographic variations of all four VEP signals across the visual field were better captured by V1 models, explaining between 2 and 6 times more variance than V2/V3. Models with all three visual areas improved fit further, but complementary analyses of temporal dynamics across all three signals indicated that the bulk of extrastriate contributions occur considerably later than V1. Overall, our data support the use of peak C1 amplitude and SSVEPs to probe V1 activity, although the SSVEP contains stronger extrastriate contributions. Moreover, we provide elaborated heuristics to distinguish visual areas in VEP data based on signal lateralisation as well as polarity inversion.

GIS and Machine Learning Models Target Dynamic Settlement Patterns and Their Driving Mechanisms from the Neolithic to Bronze Age in the Northeastern Tibetan Plateau

Traditional GIS-based statistical models are intended to extrapolate patterns of settlements and their interactions with the environment. They contribute significantly to our knowledge of past human–land relationships. Yet, these models are often criticized for their empiricism, lopsided specific factors, and for overlooking the synergy between variables. Though largely untested, machine learning and artificial intelligence methods have the potential to overcome these shortcomings comprehensively and objectively. The northeastern Tibetan Plateau (NETP) is characterized by diverse environments and significant changes to the social system from the Neolithic to Bronze Age. In this study, this area serves as a representative case for assessing the complex relationships between settlement locations and geographic environments, taking full advantages of these new models. We have explored a novel modeling case by employing GIS and random forests to consider multiple factors, including terrain, vegetation, soil, climate, hydrology, and land suitability, to construct classification models identifying environmental variation across different cultural periods. The model exhibited strong performance and a high archaeological prediction value. Potential living maps were generated for each cultural stage, revealing distinct environmental selection strategies from the Neolithic to Bronze Age. The key environmental parameters of elevation, climate, soil erosion, and cultivated land suitability were calculated with high weights, influencing human environmental decisions synergistically. Furthermore, we conducted a quantitative analysis of temporal dynamics in climate and subsistence to understand driving mechanisms behind environmental strategies. These findings suggest that past human environmental strategies were based on the comprehensive consideration of various factors, coupled with their social economic scenario. Such subsistence-oriented activities supported human beings in overcoming elevation limitation, and thus allowed them to inhabit wider pastoral areas. This study showcases the potential of machine learning in predicting archaeological probabilities and in interpreting the environmental influence on settlement patterns.

Temporal Dynamics and Trend Analysis of Areal Rainfall in Muger Subwatershed, Upper Blue Nile, Ethiopia

This study was employed to investigate the temporal variability and trend analysis of areal rainfall in the Muger subwatershed, Upper Blue Nile, Ethiopia. The study was run over the following procedures to handle the main objective: (1) determining the areal rainfall from gauged point rainfall using the Thiessen polygon method, (2) grouping the months in the season according to the study area, (3) evaluating the temporal dynamics of annual and seasonal areal rainfall using the coefficient of variation (CV), standard anomaly index (SAI), and precipitation concentration index (PCI), and (4) analyzing the trend of annual and seasonal areal rainfall using modified Mann–Kendall’s (modifiedmk) test in RStudio. Based on the temporal variability analysis, CV results depict that annual and summer areal rainfall had low variability with values of 13.43% and 13.7%, respectively. Winter and spring areal rainfall shows high variation with a CV value of 50.5% and 36%, respectively. According to the SAI output, around 70% of the considered year was in the normal condition of wetness. On the other hand, the seasonal (winter, spring, and summer) rainfall distribution of the study area shows strong irregularity distribution throughout the considered years as a result of PCI with a value of 57.5%. The trend of the areal rainfall was shown to be both increasing and decreasing. However, the trend was insignificant with a 10% confidence level.

Bifurcation to complex dynamics in largely modulated voltage-controlled parametric oscillator

An experimental demonstration of a parametric oscillation of a magnetization in a ferromagnet was performed recently by applying a microwave voltage, indicating the potential to be applied in a switching method in non-volatile memories. In the previous works, the modulation of a perpendicular magnetic anisotropy field produced by the microwave voltage was small compared with an external magnetic field pointing in an in-plane direction. A recent trend is, however, opposite, where an efficiency of the voltage controlled magnetic anisotropy (VCMA) effect is increased significantly by material research and thus, the modulated magnetic anisotropy field can be larger than the external magnetic field. Here, we solved the Landau–Lifshitz–Gilbert equation numerically and investigated the magnetization dynamics driven under a wide range of the microwave VCMA effect. We evaluated bifurcation diagrams, which summarize local maxima of the magnetization dynamics. For low modulation amplitudes, the local maximum is a single point because the dynamics is the periodic parametric oscillation. The bifurcation diagrams show distributions of the local maxima when the microwave magnetic anisotropy field becomes larger than the external magnetic field. The appearance of this broadened distribution indicates complex dynamics such as chaotic and transient-chaotic behaviors, which were confirmed from an analysis of temporal dynamics.

Layer chicken microbiota: a comprehensive analysis of spatial and temporal dynamics across all major gut sections

BackgroundThe gut microbiota influences chicken health, welfare, and productivity. A diverse and balanced microbiota has been associated with improved growth, efficient feed utilisation, a well-developed immune system, disease resistance, and stress tolerance in chickens. Previous studies on chicken gut microbiota have predominantly focused on broiler chickens and have usually been limited to one or two sections of the digestive system, under controlled research environments, and often sampled at a single time point. To extend these studies, this investigation examined the microbiota of commercially raised layer chickens across all major gut sections of the digestive system and with regular sampling from rearing to the end of production at 80 weeks. The aim was to build a detailed picture of microbiota development across the entire digestive system of layer chickens and study spatial and temporal dynamics.ResultsThe taxonomic composition of gut microbiota differed significantly between birds in the rearing and production stages, indicating a shift after laying onset. Similar microbiota compositions were observed between proventriculus and gizzard, as well as between jejunum and ileum, likely due to their anatomical proximity. Lactobacillus dominated the upper gut in pullets and the lower gut in older birds. The oesophagus had a high proportion of Proteobacteria, including opportunistic pathogens such as Gallibacterium. Relative abundance of Gallibacterium increased after peak production in multiple gut sections. Aeriscardovia was enriched in the late-lay phase compared to younger birds in multiple gut sections. Age influenced microbial richness and diversity in different organs. The upper gut showed decreased diversity over time, possibly influenced by dietary changes, while the lower gut, specifically cecum and colon, displayed increased richness as birds matured. However, age-related changes were inconsistent across all organs, suggesting the influence of organ-specific factors in microbiota maturation.ConclusionAddressing a gap in previous research, this study explored the microbiota across all major gut sections and tracked their dynamics from rearing to the end of the production cycle in commercially raised layer chickens. This study provides a comprehensive understanding of microbiota structure and development which help to develop targeted strategies to optimise gut health and overall productivity in poultry production.

Bidirectional and dynamic relationships between social isolation and activities of daily living among older adults in China.

Social isolation and disability in performing activities of daily living are increasingly recognised as significant public health concerns globally. We aimed to investigate their bidirectional associations and the related temporal dynamics in Chinese older adults. We retrieved data from the six waves of the Chinese Longitudinal Healthy Longevity Survey (2002-18) and used generalised cross-lagged modelling (GCLM) to assess the bidirectional associations between social isolation and disability in performing activities of daily living. We found that higher levels of social isolation were predictive of increased scores in disabilities in performing activities of daily living. Conversely, disabilities in performing activities of daily living showed less predictive power in relation to social isolation. The temporal dynamics analysis indicated a peak in the bidirectional associations after approximately six years, followed by decreasing trends. Our results indicate that social isolation is dominant in the bidirectional relationship. Efforts focusing on reducing it can potentially minimise disabilities in performing activities of daily living among older adults. Reinstating preventive interventions beyond the six-year mark could help maintain their effectiveness.

Linear and non-linear dynamics of ecosystem services supply, demand, and mismatches across a rapidly urbanizing region

Achieving the sustainability of coupled human-nature systems requires matching the ecosystem services (ES) provided by nature to the needs of society. However, most of the current studies on ES supply–demand relationships focus on spatial mismatches and rarely consider their temporal dynamics. In this study, the year-by-year dynamics (2001–2020) of 12 ESs at multiple spatial scales were investigated by using space–time interaction (STI) analysis, fuzzy C-mean clustering, and emerging hotspot analysis. We found that (1) three forms of temporal dynamics of ES indicators can be identified, including linear dynamics, fluctuating dynamics, and regime shift dynamics. (2) Temporal dynamic patterns of the same ES were not consistent across all counties, showing great spatial heterogeneity. The temporal dynamics of each ES type at each county were spatially explicitly clustered into linear dynamics, fluctuating dynamics, and regime shift dynamics with different means. (3) The ES supply–demand relationships can shift between supply–demand balance, excess supply, and excess demand when the temporal dynamics of ES supply do not match with that of ES demand. Our results suggest that temporal dynamic analysis in ecological restoration effectiveness assessment can help to avoid erroneous results compared to considering only pre- and post-policy ES changes. The identified emerging hotspots of excess supply and excess demand can help decision-makers more accurately identify priority locations for management.

Unraveling T-cell dynamics using fluorescent timer: Insights from the Tocky system.

Understanding the temporal dynamics of T-cell transcription is crucial for insights into immune cell function and development. In this study, we show the features of the Timer-of-Cell-Kinetics-and-Activity (Tocky) system, which enables analysis of temporal dynamics of cell activities and differentiation, leveraging Fluorescent Timer protein, which spontaneously changes its emission spectrum from blue to red fluorescence in known kinetics, as reporters. The current study examines the properties of the Tocky system, highlighting the Timer-Angle approach, which is a core algorithm of Tocky analysis and converts Timer Blue and Red fluorescence into Timer Angle and Intensity by trigonometric transformation. Importantly, Tocky analyzes time-related events within individual cells by the two phases of measurements, distinguishing between (1) the temporal sequence of cellular activities and differentiation within the time domain, and (2) the transcription frequency within the frequency domain. The transition from time measurement to frequency analysis, particularly at the Persistent locus that bridges these domains, highlights that system's unique property in what is measured and analyzed by Tocky. Intriguingly, the sustained transcriptional activities observed in cells at the Persistent locus may have unique biological features as demonstrated in activated regulatory T-cells (Treg) and pathogenic T-cells, respectively, using Foxp3-Tocky and Nr4a3-Tocky models. In conclusion, the Tocky system can provide crucial data for advancing our understanding of T-cell dynamics and function.

Accelerating dynamic time warping for speech recognition with SSE

This study presents a significant enhancement to the Dynamic Time Warping (DTW) algorithm for real-time applications like speech recognition. Through integration of SIMD (Single Instruction Multiple Data) instructions to distance function, the research demonstrates how SSE accelerates DTW, markedly reducing computation time. The paper not only explores the theoretical aspects of DTW and this optimization but also provides empirical evidence of its effectiveness. Diverse dataset of 18 voice command classes was assembled, recorded in controlled settings to ensure audio quality. The audio signal of each speech sample was segmented into frames for detailed analysis of temporal dynamics. DTW search was performed on features set based on Mel Frequency Cepstral Coefficients (MFCC) and Linear Predictive Coding (LPC), combined with delta features. A comprehensive set of 27 features was extracted from each frame to capture critical speech characteristics. The core of the study involved applying traditional DTW as a baseline for performance comparison with the SSE-optimized DTW. The evaluation, focusing on computational time, included measurements like minimum, maximum, average, and total computation times for both standard and SSE-optimized implementations. Experimental results, conducted on datasets ranging from 5 to 60 WAV files per class, revealed that the SSE-optimized DTW significantly outperformed the standard implementation across all dataset sizes. Particularly noteworthy was the consistent speed of the SSE-optimized Manhattan and Euclidean distance functions, which is crucial for real-time applications. The SSE-optimized DTW maintained a low average time, demonstrating remarkable stability and efficiency, especially with larger datasets. The study illustrates the potential of SSE optimizations in speech recognition, emphasizing the SSE-optimized DTW's capability to efficiently process large datasets.

A review of skin immune processes in acne.

Acne vulgaris is one of the most prevalent skin conditions, affecting almost all teenagers worldwide. Multiple factors, including the excessive production of sebum, dysbiosis of the skin microbiome, disruption of keratinization within hair follicles, and local inflammation, are believed to trigger or aggravate acne. Immune activity plays a crucial role in the pathogenesis of acne. Recent research has improved our understanding of the immunostimulatory functions of microorganisms, lipid mediators, and neuropeptides. Additionally, significant advances have been made in elucidating the intricate mechanisms through which cutaneous innate and adaptive immune cells perceive and transmit stimulatory signals and initiate immune responses. However, our understanding of precise temporal and spatial patterns of immune activity throughout various stages of acne development remains limited. This review provides a comprehensive overview of the current knowledge concerning the immune processes involved in the initiation and progression of acne. Furthermore, we highlight the significance of detailed spatiotemporal analyses, including analyses of temporal dynamics of immune cell populations as well as single-cell and spatial RNA sequencing, for the development of targeted therapeutic and prevention strategies.