Search Results (348)

Hydraulic shear has been widely accepted as one of the essential factors modulating phytoplankton growth. Previous experimental studies of algal growth have been conducted at the macroscopic level, and direct observation at the cell scale has been lacking. In this study, an algal-cell dynamic continuous observation platform (ACDCOP) is proposed with a parallel-plate flow chamber (PPFC) to capture cellular growth images which are then used as input to a computer vision algorithm featuring a pre-trained backpropagation neural network to quantitatively evaluate the volumes and volumetric growth rates of individual cells. The platform was applied to investigate the growth of Scenedesmus quadricauda cells under different hydraulic shear stress conditions. The results indicated that the threshold shear stress for the development of Scenedesmus quadricauda cells was 270 µL min⁻¹ (5.62 × 10⁻⁵ m² s⁻³). Cellular growth was inhibited at very low and very high intensities of hydraulic shear. Among all the experimental groups, the longest growth period for a cell, from attachment to PPFC to cell division, was 5.7 days. Cells with larger initial volumes produced larger volumes at division. The proposed platform could provide a novel approach for algal research by enabling direct observation of algal growth at the cell scale, and could potentially be applied to investigate the impacts of various environmental stressors such as nutrient, temperature, and light on cellular growth in different algal species. Full article

Background: Urine microbial analysis is a frequently requested test that is often associated with contamination during specimen collection or storage, which leads to false-positive diagnoses and delayed reporting. In the era of digitalization, machine learning (ML) can serve as a valuable tool to support clinical decision-making. Methods: This study investigates the application of a simple artificial neural network (ANN) to pre-identify negative and contaminated (false-positive) specimens. An ML model was developed using 8181 urine samples, including cytology, dipstick tests, and culture results. The dataset was randomly split 2:1 for training and testing a multilayer perceptron (MLP). Input variables with a normalized importance below 0.2 were excluded. Results: The final model used only microbial and either urine color or urobilinogen pigment analysis as inputs; other physical, chemical, and cellular parameters were omitted. The frequency of positive and negative specimens for bacteria was 6.9% and 89.6%, respectively. Contaminated specimens represented 3.5% of cases and were predominantly misclassified as negative by the MLP. Thus, the negative predictive value (NPV) was 96.5% and the positive predictive value (PPV) was 87.2%, leading to 0.82% of the cultures being unnecessary microbial cultures (UMC). Conclusions: These results suggest that the MLP is reliable for screening out negative specimens but less effective at identifying positive ones. In conclusion, ANN models can effectively support the screening of negative urine samples, detect clinically significant bacteriuria, and potentially reduce unnecessary cultures. Incorporating morphological information data could further improve the accuracy of our model and minimize false negatives. Full article

Investigating land cover patterns, changes in carbon stocks, and forecasting future conditions are essential for formulating regional sustainable development strategies and enhancing ecological and environmental quality. This study centers on Guiyang, a mountainous urban area in southwestern China, to analyze the dynamic changes in land cover and their effects on carbon stocks from 2000 to 2035. A carbon stocks assessment framework was developed using a cellular automaton-based artificial neural network model (CA-ANN), the InVEST model, and the geographical detector model to predict future land cover changes and identify the primary drivers of variations in carbon stocks. The results indicate that (1) from 2000 to 2020, impervious surfaces expanded significantly, increasing by 199.73 km². Compared to 2020, impervious surfaces are projected to increase by 1.06 km², 13.54 km², and 34.97 km² in 2025, 2030, and 2035, respectively, leading to further reductions in grassland and forest areas. (2) Over time, carbon stocks in Guiyang exhibited a general decreasing trend; spatially, carbon stocks were higher in the western and northern regions and lower in the central and southern regions. (3) The level of greenness, measured by the normalized vegetation index (NDVI), significantly influenced the spatial variation of carbon stocks in Guiyang. Changes in carbon stocks resulted from the combined effects of multiple factors, with the annual average temperature and NDVI being the most influential. These findings provide a scientific basis for advancing low-carbon development and constructing an ecological civilization in Guiyang. Full article

Regional ecological environmental quality (EEQ) is a vital indicator for environmental management and supporting sustainable development. However, the absence of robust and accurate EEQ prediction models has hindered effective environmental strategies. This study proposes a novel approach to address this gap by integrating the ecological index (EI) model with several predictive models, including autoregressive integrated moving average (ARIMA), convolutional neural network (CNN), long short-term memory (LSTM), and cellular automata (CA), to forecast regional EEQ. Initially, the spatiotemporal evolution of the input data used to calculate the EI score was analyzed. Subsequently, tailored prediction models were developed for each dataset. These models were sequentially trained and validated, and their outputs were integrated into the EI model to enhance the accuracy and coherence of the final EEQ predictions. The novelty of this methodology lies not only in integrating existing predictive models but also in employing an innovative fusion technique that significantly improves prediction accuracy. Despite data quality issues in the case study dataset led to higher prediction errors in certain regions, the overall results exhibit a high degree of accuracy. A comparison of long-term EI predictions with EI assessment results reveals that the R² value for the EI score exceeds 0.96, and the kappa value surpasses 0.76 for the EI level, underscoring the robust performance of the integrated model in forecasting regional EEQ. This approach offers valuable insights into exploring regional EEQ trends and future challenges. Full article

Rapid urbanization and industrial development have significantly altered land use and cover across the globe, intensifying urban thermal environments and exacerbating the urban heat island (UHI) effect. Gujranwala, Pakistan, represents an industrial growth that has driven substantial land use/land cover (LULC) changes and temperature increases; however, the directional and distance-based patterns of these changes remain unquantified. Therefore, this study is conducted to examine spatiotemporal changes in LULC and variations in the Urban Thermal Field Variation Index (UTFVI) between 2001 and 2021 and to project future scenarios for 2031 and 2041 using (1) Earth Observation Indices (EOIs) with machine learning (ML) classifiers (Random Forest) for precise LULC mapping through the Google Earth Engine (GEE) platform, (2) Cellular Automata–Artificial Neural Networks (CA-ANNs) for future scenario projection, and (3) Gradient Directional Analysis (GDA) to quantify directional (16-axis) and distance-based (concentric zones) patterns of urban expansion and thermal variation from 2001–2021. The study revealed significant LULC changes, with built-up areas expanding by 7.5% from 2001 to 2021, especially in the east, northeast, and southeast directions within a 20 km radius. Due to urban encroachment, vegetation and cropland decreased by 1.47% and 1.83%, respectively. The urban thermal environment worsened, with the highest land surface temperature (LST) rising from 41 °C in 2001 to 55 °C in 2021. Additionally, the UTFVI showed expanding areas under the ‘strong’ and ‘strongest’ categories, increasing from 30.58% in 2001 to 33.42% in 2041. Directional analysis highlighted severe thermal stress in the southern and southwestern areas linked to industrial activities and urban sprawl. This integrated approach provides a template for analyzing urban thermal environments in developing cities, supporting targeted mitigation strategies through direction- and distance-specific planning interventions to mitigate UHI impacts. Full article

Mitotic figures in tumor tissues are an important criterion for diagnosing malignant lesions, and physicians often search for the presence of mitosis in whole slide imaging (WSI). However, prolonged visual inspection by doctors may increase the likelihood of human error. With the advancement of deep learning, AI-based automatic cytopathological diagnosis has been increasingly applied in clinical settings. Nevertheless, existing diagnostic models often suffer from high computational costs and suboptimal detection accuracy. More importantly, when assessing cellular abnormalities, doctors frequently compare target cells with their surrounding cells—an aspect that current models fail to capture due to their lack of intercellular information modeling, leading to the loss of critical medical insights. To address these limitations, we conducted an in-depth analysis of existing models and propose an Inter–Intra Hypergraph Neural Network (II-HGNN). Our model introduces a block-based feature extraction mechanism to efficiently capture deep representations. Additionally, we leverage hypergraph convolutional networks to process both intracellular and intercellular information, leading to more precise diagnostic outcomes. We evaluate our model on publicly available datasets under varying imaging conditions, and experimental results demonstrate that our approach consistently outperforms baseline models in terms of accuracy. Full article

Haploinsufficiency disorders are genetic diseases caused by reduced gene expression, leading to developmental, metabolic, and tumorigenic abnormalities. The dosage-sensitive Retinoic Acid Induced 1 (RAI1) gene, located within the 17p11.2 region, is central to the core features of Smith––Magenis syndrome (SMS) and Potocki––Lupski syndrome (PTLS), caused by the reciprocal microdeletions and microduplications of this region, respectively. SMS and PTLS present contrasting phenotypes. SMS is characterized by severe neurobehavioral manifestations, sleep disturbances, and metabolic abnormalities, and PTLS shows milder features. Here, we detail the molecular functions of RAI1 in its wild-type and haploinsufficiency conditions (RAI1+/−), as studied in animal and cellular models. RAI1 acts as a transcription factor critical for neurodevelopment and synaptic plasticity, a chromatin remodeler within the Histone 3 Lysine 4 (H3K4) writer complex, and a regulator of faulty 5′-capped pre-mRNA degradation. Alterations of RAI1 functions lead to synaptic scaling and transcriptional dysregulation in neural networks. This review highlights key molecular mechanisms of RAI1, elucidating its role in the interplay between genetics and phenotypic features and summarizes innovative therapeutic approaches for SMS. These data provide a foundation for potential therapeutic strategies targeting RAI1, its mRNA products, or downstream pathways. Full article

Despite significant advances in understanding neuronal development, a fully quantitative framework that integrates intracellular mechanisms with environmental cues during axonal growth remains incomplete. Here, we present a unified biophysical model that captures key mechanochemical processes governing axonal extension on micropatterned substrates. In these environments, axons preferentially align with the pattern direction, form bundles, and advance at constant speed. The model integrates four core components: (i) actin–adhesion traction coupling, (ii) lateral inhibition between neighboring axons, (iii) tubulin transport from soma to growth cone, and (iv) orientation dynamics guided by substrate anisotropy. Dynamical systems analysis reveals that a saddle–node bifurcation in the actin adhesion subsystem drives a transition to a high-traction motile state, while traction feedback shifts a pitchfork bifurcation in the signaling loop, promoting symmetry breaking and robust alignment. An exact linear solution in the tubulin transport subsystem functions as a built-in speed regulator, ensuring stable elongation rates. Simulations using experimentally inferred parameters accurately reproduce elongation speed, alignment variance, and bundle spacing. The model provides explicit design rules for enhancing axonal alignment through modulation of substrate stiffness and adhesion dynamics. By identifying key control parameters, this work enables rational design of biomaterials for neural repair and engineered tissue systems. Full article

The conservation and monitoring of land cover represent crucial elements for sustainable regional development, especially in fragile high Andean ecosystems. This study evaluates the spatiotemporal changes in land use and land cover (LULC) in the Locumba basin over the period of 1984–2023. A hybrid modeling approach combining artificial neural networks (ANN) and cellular automata (CA) was employed to project future changes for 2033, 2043, and 2053. The results reveal a significant reduction in glaciers and lagoons throughout the Locumba basin, with notable declines from 1984 to 2023, while vegetated areas, particularly grasslands and wetlands, experienced substantial expansion. Specifically, grasslands increased by 273.7% relative to their initial coverage, growing from 57.87 km² in 1984 to over 220.31 km² in 2023, with projections indicating continued growth to over 331.62 km² by 2053. This multitemporal analysis provides crucial information for anticipating future land dynamics and underscores the urgent need for strategic conservation planning to mitigate the continued loss of strategic ecosystems in the high Andean region of Tacna. Full article

Land use and land cover (LULC) in Northern Bangladesh have undergone substantial transformations due to both anthropogenic and natural drivers. This study examines historical LULC changes (1990–2022) and projects future trends for 2030 and 2054 using remote sensing and the Cellular Automata-Artificial Neural Network (CA-ANN) model. Multi-temporal Landsat imagery was classified with 80.75–86.23% accuracy (Kappa: 0.75–0.81). Model validation comparing simulated and actual 2014 data yielded 79.98% accuracy, indicating a reasonably good performance given the region’s rapidly evolving and heterogeneous landscape. The results reveal a significant decline in waterbodies, which is projected to shrink by 34.4% by 2054, alongside a 1.21% reduction in cropland raising serious environmental and food security concerns. Vegetation, after an initial massive decrease (1990–2014), increased (2014–2022) due to different forms of agroforestry practices and is expected to increase by 4.64% by 2054. While the model demonstrated fair predictive power, its moderate accuracy highlights challenges in forecasting LULC in areas characterized by informal urbanization, seasonal land shifts, and riverbank erosion. These dynamics limit prediction reliability and reflect the region’s ecological vulnerability. The findings call for urgent policy action particularly afforestation, water resource management, and integrated land use planning to ensure environmental sustainability and resilience in this climate-sensitive area. Full article

Chronic pain is a multifactorial and complex condition that significantly affects individuals’ quality of life. The underlying mechanisms of chronic pain involve complex alterations in neural circuits, gene expression, and cellular signaling pathways. Recently, ncRNAs, such as miRNAs, lncRNAs, circRNAs, and siRNAs, have been identified as crucial regulators in the pathophysiology of chronic pain. These ncRNAs modulate gene expression at both the transcriptional and post-transcriptional levels, affecting pain-related pathways like inflammation, neuronal plasticity, and sensory processing. miRNAs have been shown to control genes involved in pain perception and nociceptive signaling, while lncRNAs interact with chromatin remodeling factors and transcription factors to modify pain-related gene expression. CircRNAs act as sponges for miRNAs, thereby influencing pain mechanisms. siRNAs, recognized for their gene-silencing capabilities, also participate in regulating the expression of pain-related genes. This review examines the diverse roles of ncRNAs in chronic pain, emphasizing their potential as biomarkers for pain assessment and as targets for novel therapeutic strategies. A profound understanding of the ncRNA-mediated regulatory networks involved in chronic pain could result in more effective and personalized pain management solutions. Full article

Indonesia faces significant challenges in meeting food security targets due to rapid agricultural land loss, with approximately 1.22 million hectares of rice fields converted between 1990 and 2022. Therefore, this study developed a prediction model for the loss of rice fields by 2030, incorporating land productivity attributes, specifically rice cropping intensity/RCI, using geospatial technology—a novel method with a resolution of approximately 10 m for quantifying ecosystem service (ES) impacts. Land use/land cover data from Landsat images (2013, 2020, 2024) were classified using the Random Forest algorithm on Google Earth Engine. The prediction model was developed using a Multi-Layer Perceptron Neural Network and Markov Cellular Automata (MLP-NN Markov-CA) algorithms. Additionally, time series Sentinel-1A satellite imagery was processed using K-means and a hierarchical clustering analysis to map rice fields and their RCI. The validation process confirmed high model robustness, with an MLP-NN Markov-CA accuracy and Kappa coefficient of 83.90% and 0.91, respectively. The present study, which was conducted in Indramayu Regency (West Java), predicted that 1602.73 hectares of paddy fields would be lost within 2020–2030, specifically 980.54 hectares (61.18%) and 622.19 hectares (38.82%) with 2 RCI and 1 RCI, respectively. This land conversion directly threatens ES, resulting in a projected loss of 83,697.95 tons of rice production, which indicates a critical degradation of service provisioning. The findings provide actionable insights for land use planning to reduce agricultural land conversion while outlining the urgency of safeguarding ES values. The adopted method is applicable to regions with similar characteristics. Full article

Odontogenesis, the development of teeth, is a complex, multistage process that unfolds from early embryogenesis through tooth eruption and maturation. It serves as a classical model of organogenesis due to the intricate reciprocal interactions between cranial neural crest-derived mesenchyme and oral epithelium. This narrative review synthesizes current scientific knowledge on human tooth development, tracing the journey from the embryological origins in the first branchial arch to the formation of a fully functional tooth and its supporting structures. Key morphogenetic stages—bud, cap, bell, apposition, and root formation—are described in detail, highlighting the cellular events and histological features characterizing each stage. We discuss the molecular and cellular regulatory networks that orchestrate odontogenesis, including the conserved signaling pathways (Wnt, BMP, FGF, SHH, EDA) and transcription factors (e.g., PAX9, MSX1/2, PITX2) that drive tissue patterning and cell differentiation. The coordinated development of supporting periodontal tissues (cementum, periodontal ligament, alveolar bone, gingiva) is also examined as an integral part of tooth organogenesis. Finally, developmental anomalies (such as variations in tooth number, size, and form) and the fate of residual embryonic epithelial cells are reviewed to underscore the clinical significance of developmental processes. Understanding the normal course of odontogenesis provides crucial insight into congenital dental disorders and lays a foundation for advances in regenerative dental medicine. Full article

Single-cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq) technology enables single-cell resolution analysis of chromatin accessibility, offering critical insights into gene regulation, epigenetic heterogeneity, and cellular differentiation across various biological contexts. However, existing cell annotation methods face notable limitations. Cross-omics approaches, which rely on single-cell RNA sequencing (scRNA-seq) as a reference, often struggle with data alignment due to fundamental differences between transcriptional and chromatin accessibility modalities. Meanwhile, intra-omics methods, which rely solely on scATAC-seq data, are frequently affected by batch effects and fail to fully utilize genomic sequence information for accurate annotation. To address these challenges, we propose scAttG, a novel deep learning framework that integrates graph attention networks (GATs) and convolutional neural networks (CNNs) to capture both chromatin accessibility signals and genomic sequence features. By utilizing the nucleotide sequences corresponding to scATAC-seq peaks, scAttG enhances both the robustness and accuracy of cell-type annotation. Experimental results across multiple scATAC-seq datasets suggest that scAttG generally performs favorably compared to existing methods, showing competitive performance in single-cell chromatin accessibility-based cell-type annotation. Full article

In a world experiencing rapid urbanization, the phenomenon of land surface temperature (LST) variation has invited substantial attention due to its profound impact on the environment and human well-being. Changes in land use and land cover (LULC) within urban areas significantly influence the dynamics of LST and are a major driver of urban eco-environmental change. The complex connections between LULC dynamics, LST, and climate change are investigated in this systematic review, with a focus on the combined effects of these variables and the use of Machine Learning (ML) techniques. The data in this study, based on peer-reviewed publications from the past 25 years, were obtained from Science Direct and Web of Science databases. Based on our findings, Landsat is the most widely used dataset for analyzing the impacts of LULC on LST. Additionally, built-up areas, vegetation, and population density had the biggest effects on LST values based on focused studies. This systematic review reveals that Artificial Neural Networks (ANNs), Cellular Automata-Markov (CA-Markov), and Random Forest (RF) are the most used ML techniques in predicting LULC and LST. The study finds that NDBI and NDVI are recognized as the key LULC indices that have strong correlations with LST. We also highlight key LULC classes that have the most impact on LST variation. To validate the results, these studies employ Pearson correlation, the NDVI and NDBI index, and other linear regression methods. This review concludes by highlighting future research directions and the current need for interdisciplinary efforts to address the intricate dynamics of LULC and the Earth’s surface temperature. Full article