Search Results (12,289)

The objective of this work was to assess different concentrations of a novel formulation of 1-aminocyclopropane-1-carboxylic acid (ACC) on anthocyanin accumulation and color development, as well as on the physicochemical characteristics of the ‘Benitaka’ table grape grown in a subtropical region in two application forms. The trial was conducted on a commercial property located in a subtropical area in Brazil in 2022. Treatments included different concentrations of a new formulation containing 400 g kg⁻¹ of ACC, ranging from 0 to 125 g 100 L⁻¹, as well as a standard concentration of a formulation containing 100 g L⁻¹ of abscisic acid (S-ABA): 3.2 L ha⁻¹. The exogenous application of ACC was performed at the beginning of berry ripening (véraison), while that of S-ABA was performed twice: the first, at véraison, and the second, 7 days later. The concentration of total anthocyanins, berry color index, physicochemical characteristics, and sensory–visual analysis of color coverage of the bunches were evaluated weekly, while berry firmness was appraised at harvest. A single exogenous application of ACC or two applications of S-ABA resulted in daily increment rates that provided a high accumulation of total anthocyanins, as well as greater berry color development, regardless of the application method, directed to the canopy of the vines or only to the bunches. As a result, the new formulation of ACC at concentrations of 75 g to 100 g 100 L⁻¹ is a novel tool to stimulate the anthocyanins accumulation and berry color development in ‘Benitaka’ table grapes grown in subtropical regions without negative impact on bunches or vines. Full article

Wide-angle X-ray diffraction analysis is a powerful tool for investigating the structure and orientation of cellulose microfibrils in plant cell walls, but the complex relationship between diffraction patterns and underlying structural parameters remains challenging to both understand and validate. This study presents a comprehensive dataset of 81,906 Monte Carlo-simulated wide-angle X-ray diffraction patterns for the cellulose Iβ 200 lattice. The dataset was generated using a mechanistic, physically informed simulation procedure that incorporates realistic cell wall geometries from wood anatomy, including circular and polygonal fibers, and accounts for the full range of crystallographic and anatomical parameters influencing diffraction patterns. Each simulated pattern required multiple nested Monte Carlo iterations, totaling approximately 10 million calculations per pattern. The resulting dataset pairs each diffraction pattern with its exact generating parameter set, including mean microfibril angle (MFA), MFA variability, fiber tilt angles, and cell wall cross-sectional shape. The dataset addresses a significant barrier in the field—the lack of validated reference data with known ground truth values for testing and developing new analytical methods. It enables the development, validation, and benchmarking of novel algorithms and machine learning models for MFA prediction from diffraction patterns. The simulated data also allow for systematic investigation of the effects of geometric factors on diffraction patterns and serves as an educational resource for visualizing structure–diffraction relationships. Despite some limitations, such as assuming ideal diffraction conditions and focusing primarily on the S2 cell wall layer, this dataset provides a valuable foundation for advancing X-ray diffraction analysis methods for cellulose microfibril architecture characterization in plant cell walls. Full article

In this study, we proposed a method for detecting chips in the mouth of glass bottles using machine learning. In recent years, Japanese cosmetic glass bottles have gained attention for their advancements in manufacturing technology and eco-friendliness through the use of recycled glass, leading to an increase in the volume of glass bottle exports overseas. Although cosmetic bottles are subject to strict quality inspections from the standpoint of safety, the complicated shape of the glass bottle mouths makes automated inspections difficult, and visual inspections have been the norm. Visual inspections conducted by workers have become problematic because it has become clear that the standard of judgment differs from worker to worker and that inspection accuracy deteriorates after long hours of work. To address these issues, the development of inspection systems for glass bottles using image processing and machine learning has been actively pursued. While conventional image processing methods can detect chips in glass bottles, the target glass bottles are those without screw threads, and the light from the light source is diffusely reflected by the screw threads in the glass bottles in this study, resulting in a loss of accuracy. Additionally, machine learning-based inspection methods are generally limited to the body and bottom of the bottle, excluding the mouth from analysis. To overcome these challenges, this study proposed a method to extract only the screw thread regions from the bottle image, using a dedicated machine learning model, and perform defect detection. To evaluate the effectiveness of the proposed approach, accuracy was assessed by training models using images of both the entire mouth and just the screw threads. Experimental results showed that the accuracy of the model trained using the image of the entire mouth was 98.0%, while the accuracy of the model trained using the image of the screw threads was 99.7%, indicating that the proposed method improves the accuracy by 1.7%. In a demonstration experiment using data obtained at a factory, the accuracy of the model trained using images of the entire mouth was 99.7%, whereas the accuracy of the model trained using images of screw threads was 100%, indicating that the proposed system can be used to detect chips in factories. Full article

There has been an increasing number of calls to apply new technologies to learning contexts for STEM education. However, limited studies have explored the role of technology in bridging teachers and children to create STEM knowledge collaboratively. Therefore, early childhood teachers encounter challenges integrating digital technologies to support children’s STEM learning. The challenges include developing effective and innovative scaffolding strategies to incorporate digital technology and visualize the processes of using technologies in children’s STEM knowledge building. This study reports on an in-depth exploratory case study from a kindergarten classroom in Hong Kong, exemplifying a new approach to integrating digital technologies within spatialized STEM learning. The case selected continuity learning episodes from a spatially directed STEM learning unit on making a safe traffic city. Under digital technology-integrated scaffolding, the teacher and children co-created a traffic symbolic system by designing symbols of landmarks, developing and applying spatial language, making maps and traffic games with rules. The thematic analysis was adopted to analyze the teachers’ STEM activity plans and reflective reports. The finding indicated that the process through which the teacher and children collaboratively created STEM knowledge via technology-integrated scaffolding involved recalling spontaneous understanding about everyday concepts, exploring ideas in authentic contexts, sorting and organizing their collected information, and identifying and correlating abstract concepts with corresponding everyday practices. The children required two levels of technology-integrated scaffolding strategies to engage in STEM knowledge collaborative creation: scaffolding for technology using and scaffolding through use of technology. Three novel roles of technology emerged that transform learning from knowledge delivery to collaborative creation in inquiries STEM tasks for young children: application, mediator, and catalyst. The study also highlights teachers and children transforming into new roles in knowledge collaborative creation processes in spatialized STEM learning under the technology-integrated scaffolding strategies. Moreover, it spotlights the reconceptualization of the STEM learning culture in the technology-integrated knowledge co-create classroom from teacher-centered to more open child-centered learning. Full article

In the image era, architectural drawing gradually evolved from being a part of traditional architectural design to an artistic form with independent aesthetic value. However, a systematic evaluation method for this unique art form is still lacking. This study analyzes the works of four Chinese and Japanese architects, focusing on the functionality and artistry of architectural drawings. Combining iconography, semiotic analysis, and theories from visual culture studies, it explores the visual language and cultural significance embedded in architectural drawings from a new perspective and attempts to establish an evaluation framework. The analysis of visual symbols, cultural codes, and social contexts reveals how architects convey architectural concepts, historical memories, and urban landscapes through their drawings. This study finds that architectural drawings not only convey architectural information but also integrate cultural narratives and artistic expression, serving as an important intersection between architecture and other disciplines. Although interpretations may vary across cultural contexts, the semiotic approach offers a relatively objective evaluation system. This research helps architects, artists, and educators better understand the role of architectural drawing and promotes its application in architectural design, artistic creation, and education. Full article

The menstrual cycle represents a fundamental biological rhythm in a woman’s life. This study aims to analyse the potential influence of the menstrual cycle on female athletic performance, specifically focusing on variations in body composition, muscle oxygen saturation, and post-exertion recovery. The sample consisted of a 21-year-old female athlete (a former elite-level basketball player), who performed a Bulgarian Split Squat test once a week throughout a complete menstrual cycle. In the data analysis, the menstrual cycle was verified using biological and hormonal markers, the coefficient of variation in muscle oxygen saturation was calculated, and visual inspection was employed to assess the observed curves. The results indicated minor variations in muscle mass (ranging from 38.8 kg to 40.4 kg) and fat mass (10.7 kg to 11.9 kg) across different phases of the cycle. Additionally, an increase in force production (4–5 repetitions increasing to 13–14) was observed, likely due to elevated oestrogen levels in the bloodstream. In conclusion, the menstrual cycle should be considered when designing training programmes for female athletes, ensuring an individualised approach that accounts for hormonal fluctuations and their impact on performance. Full article

The development of neural networks has made the introduction of multimodal systems inevitable. Computer vision methods are still not widely used in biological research, despite their importance. It is time to recognize the significance of advances in feature extraction and real-time analysis of information from cells. Teacherless learning for the image clustering task is of great interest. In particular, the clustering of single cells is of great interest. This study will evaluate the feasibility of using latent representation and clustering of single cells in various applications in the fields of medicine and biotechnology. Of particular interest are embeddings, which relate to the morphological characterization of cells. Studies of C2C12 cells will reveal more about aspects of muscle differentiation by using neural networks. This work focuses on analyzing the applicability of the latent space to extract morphological features. Like many researchers in this field, we note that obtaining high-quality latent representations for phase-contrast or bright-field images opens new frontiers for creating large visual-language models. Graph structures are the main approaches to non-Euclidean manifolds. Graph-based segmentation has a long history, e.g., the normalized cuts algorithm treated segmentation as a graph partitioning problem—but only recently have such ideas merged with deep learning in an unsupervised manner. Recently, a number of works have shown the advantages of hyperbolic embeddings in vision tasks, including clustering and classification based on the Poincaré ball model. One area worth highlighting is unsupervised segmentation, which we believe is undervalued, particularly in the context of non-Euclidean spaces. In this approach, we aim to mark the beginning of our future work on integrating visual information and biological aspects of individual cells to multimodal space in comparative studies in vitro. Full article

Purpose: To assess the feasibility and effectiveness of remote monitoring for patients with retinal vein occlusion (RVO) treated with intravitreal anti-VEGF injections. Methods: A retrospective analysis was conducted at the Eye Clinic of the University of Catania. Thirty-four eyes of 34 patients with RVO were included for a 12-month follow-up period. After a comprehensive baseline ophthalmic examination, the patients received a loading treatment consisting of three monthly intravitreal injections of anti-VEGF, followed by monthly or bimonthly remote follow-up visits at peripheral centers. Optical coherence tomography (OCT) images and clinical data were shared online with our eye clinic for remote evaluations. Data on hospital and peripheral center visits, intravitreal injections, and OCT scans were collected and analyzed. Results: The patients had an average of 5.71 ± 1.14 visits to peripheral centers and 2.1 ± 0.8 visits to our center for fluorescein angiography. The mean number of injections was 5.26 ± 1.29 and the mean improvement in best-corrected visual acuity (BCVA) was 11.47 ± 5.56 letters. Remote OCT evaluations accounted for 194 scans, there was a high agreement between two expert in-hospital examinators (Cohen’s κ = 0.927) with only 14 cases requiring hospital visits for inconclusive results. Conclusion: Remote monitoring for RVO patients significantly reduced hospital admissions for follow-up visits, reducing the clinical burden on medical staff, patients, and caregivers, while maintaining reliable patient assessments. Full article

At present, remote sensing serves as a key approach to track ecological recovery after fires. However, systematic and quantitative research on the research progress of post-fire remote sensing remains insufficient. This study presents the first global bibliometric analysis of post-fire remote sensing research (1994–2024), analyzing 1155 Web of Science publications and using CiteSpace to reveal critical trends and gaps. The key findings include the following: As multi-sensor remote sensing and big data technologies evolve, the research focus is increasingly pivoting toward interdisciplinary, multi-scale, and intelligent methodologies. Since 2020, AI-driven technologies such as machine learning have become research hotspots and continue to grow. In the future, more extensive time-series monitoring, holistic evaluations under compound disturbances, and enhanced fire management strategies will be required to addressing the global climate change challenge and sustainability. The USA, Canada, China, and multiple European nations work jointly on fire ecology research and technology development, but Africa, as a high wildfire-incidence area, currently lacks appropriate local research. Remote sensing of the environment and remote sensing and forests maintain a pivotal role in scholarly impact and information exchange. This work redefines post-fire remote sensing as a nexus of ecological urgency and social justice, demanding inclusive innovation to address climate-driven post-fire recovery regimes. Full article

Despite the widespread use of information measures in analyzing probabilistic systems, effective visualization tools for understanding complex dependencies in sequential data are scarce. In this work, we introduce the information matrix (InfoMat), a novel and intuitive matrix representation of information transfer in sequential systems. InfoMat provides a structured visual perspective on mutual information decompositions, enabling the discovery of new relationships between sequential information measures and enhancing interpretability in time series data analytics. We demonstrate how InfoMat captures key sequential information measures, such as directed information and transfer entropy. To facilitate its application in real-world datasets, we propose both an efficient Gaussian mutual information estimator and a neural InfoMat estimator based on masked autoregressive flows to model more complex dependencies. These estimators make InfoMat a valuable tool for uncovering hidden patterns in data analytics applications, encompassing neuroscience, finance, communication systems, and machine learning. We further illustrate the utility of InfoMat in visualizing information flow in real-world sequential physiological data analysis and in visualizing information flow in communication channels under various coding schemes. By mapping visual patterns in InfoMat to various modes of dependence structures, we provide a data-driven framework for analyzing causal relationships and temporal interactions. InfoMat thus serves as both a theoretical and empirical tool for data-driven decision making, bridging the gap between information theory and applied data analytics. Full article

Digital pedagogies have significant potential to enhance classroom learning, and teachers are increasingly seeking ways to integrate these approaches. Combining video with drama provides students with opportunities to explore technology while expressing themselves through dramatic performance. This article presents a qualitative case study exploring the use of creative videomaking as a literacy strategy in an upper primary class at a high-diversity Australian school. The research explored different forms of literacy involved in collaborative videomaking, as well as benefits and challenges associated with this approach. Thematic analysis of observations, interviews, and student videos identified collaborative drama and videomaking as an engaging and inclusive pedagogy for diverse learners. Benefits included a strong focus on oral and visual communication and an authentic use of digital technologies. Written literacy would have benefitted from separate sessions targeting scriptwriting, however, and although critical digital topics captured students’ interest, these also needed more time than was allocated. Other challenges included increased self-consciousness for some students when recording their voices, limitations of filming in a classroom, and additional time needed for lesson preparation. Further findings showed drama strategies were particularly useful for improving at-risk students’ confidence and sense of identity as learners and speakers of English. Overall, integrating videomaking into literacy instruction effectively fostered multimodal and technological literacy, creativity, and identity for diverse students. Full article

Precise segmental spinal analysis during gait has various implications for clinical use and basic research. Here, we report the use of Surface Topography (ST) to analyze three-dimensional spinal segment movements, in combination with foot pressure measuring, to describe individual vertebral bodies’ motion relative to specific phases of gait. Using Statistical Analysis System (SAS) scripts, single files were merged into one raw data table and were used to generate a standardized gait cycle (SGC) for each measurement, including all measured gait cycles for each individual patient, with a spline function to obtain smooth curve progressions. Graph templates from Statistical Package for the Social Sciences create detailed visualizations of the SGCs. Previously obtained measurements from healthy participants were used to demonstrate possible applications of our method. An impressive inter-individual variability as well as intra-individual consistency of spinal motion is shown. The transformation into an SGC facilitates intra- and inter-individual comparisons for qualitative and quantitative analyses. In future studies, we want to use this method to distinguish between physiologic and pathologic spinal motion. Artificial intelligence-based analysis can facilitate this process. All tools and visualizations used are freely available in repositories to enable the replication and validation of our findings. Full article

This study presents a comprehensive genetic characterization of the H9c2 cell line, a widely used model for cardiac myoblast research. We established a short tandem repeat (STR) profile for H9c2 that is useful to confirm the identity and stability of the cell line. Additionally, we prepared H9c2 metaphase chromosomes and performed karyotyping and molecular cytogenetics to further investigate chromosomal characteristics. The genetic analysis showed that H9c2 cells exhibit chromosomal instability, which may impact experimental reproducibility and data interpretation. Next-generation sequencing (NGS) was performed to analyze the transcriptome, revealing gene expression patterns relevant to cardiac biology. Western blot analysis further validated the expression levels of selected cardiac genes identified through NGS. Additionally, Phalloidin staining was used to visualize cytoskeletal organization, highlighting the morphological features of these cardiac myoblasts. Our findings collectively support that H9c2 cells are a reliable model for studying cardiac myoblast biology, despite some genetic alterations identified resembling sarcoma cells. The list of genes identified through NGS analysis, coupled with our comprehensive genetic analysis, will serve as a valuable resource for future studies utilizing this cell line in cardiovascular medicine. Full article

Background/Objectives: The COVID-19 pandemic has significantly affected public health and social behavior, contributing to increased psychoactive substance (PAS) use due to social isolation, economic stress, and uncertainty. This study aims to assess the impact of the pandemic on alcohol, cannabinoid, and opioid consumption among drivers involved in road traffic accidents (RTAs) in Kazakhstan. Understanding these patterns is essential for improving public health policies and road safety measures during crises. Methods: This retrospective cross-sectional study analyzed medical records from the Digital System of Medical Examination, a national database of drivers involved in traffic accidents in Kazakhstan. This study included 157,490 anonymized records from 1 January 2019, to 31 December 2020, categorizing cases into pre-COVID-19 and COVID-19 groups on the basis of the first nationwide lockdown on 16 March 2020. Statistical analyses, including prevalence rates and relative changes, were conducted via SPSS 20, while spatial distributions were visualized via QGIS software. Results: An analysis of all the records revealed a 12.9% decline in traffic accidents during the pandemic, with male drivers predominating during both periods. The mean age of the drivers in the compared groups was 36. Alcohol and cannabinoid use significantly increased during the COVID-19 period by 3.71% and 11.51%, respectively. In contrast, opioid use declined by 10.00%, but the difference was not statistically significant. The greatest increase in positive alcohol tests among drivers was observed in the Atyrau (94.80%), Pavlodar (35.43%), and North Kazakhstan (31.02%) regions, and Atyrau also presented the greatest increase in cannabinoid-positive cases. Conclusions: The results indicate that the COVID-19 pandemic and related lockdown measures have affected PAS consumption patterns among drivers. These findings are crucial for informing policies and developing strategies to improve road safety during future public health emergencies. Full article

Objective: Because of the evolving nature of tissue engineering scaffolds in the treatment of spinal cord injury (SCI), the current study was carried out to evaluate the research productivity of tissue engineering scaffolds in the treatment of SCI. Methods: Studies published from 2000 to 2025 were retrieved from the Web of Science core collection with topics of spinal cord injury and tissue engineering scaffolds. The data were analyzed and visualized using the VOSviewer network analysis software. Results: Among 1542 articles analyzed, annual publications surged from 2000 to 2019, stabilizing thereafter. The U.S., China, and Canada led in productivity, with Northwestern University and the Biomaterials journal being top contributors. Keyword analysis revealed research hotspots such as functional recovery, axonal regeneration, stem cells, and hydrogels. Notably, hydrogels embedded with genetically engineered cells emerged as a pivotal trend, reflecting a shift toward biomimetic and combinatorial therapies. Collaboration networks highlighted intensified partnerships between Chinese and North American institutions, signaling global interdisciplinary efforts. Conclusions: This study provides the first bibliometric roadmap for tissue engineering scaffolds in SCI, identifying key trends, influential entities, and underexplored areas. The rise in hydrogels and international collaborations underscores opportunities for targeted research. These findings guide researchers in prioritizing high-impact journals, fostering partnerships, and advancing novel scaffold designs to bridge translational gaps in SCI treatment. Full article