Entire Development Process Research Articles

Applications of 3D Printing Technology in Angiogenesis Model for Cancer Research

3D printing is a technology that has gradually advanced with the development of technology and has been used in medicine and cancer treatment. 3D printing's ability to prototype quickly allows it to quickly convert fundamental cancer research findings into therapeutic applications, which could speed up or possibly completely transform the entire medication discovery and development process. Angiogenesis is an essential process in normal development and adult physiology but is disrupted in many diseases. The review summarizes the applications of 3D printing technology in angiogenesis models for cancer research. 3D printed PLA-bio-glass composite for bone tissue engineering can be used to study bone healing and regeneration. The in vitro microvascular model using 3D printing technology can be applied to explore the role of angiogenesis-related proteins. A 2D or 3D histological model is sufficient to track cancer cell dynamics. A combination of 3D bio-printed blood vessel models and spheroids can be a cost-effective and efficient method for drug testing to evaluate both tumor growth and angiogenesis. In future research, it can be expected to develop technologies to support 4D printing of personalized adjustable bionic periosteum with anisotropic microstructures to accelerate angiogenesis and bone healing.

Beyond the usual suspects: contrasting perspectives of developers and peripheral users of model-based energy scenarios

Beyond the usual suspects: contrasting perspectives of developers and peripheral users of model-based energy scenarios

Uncovering protein regulation during adventitious root formation in Platycladus orientalis cuttings.

Cell totipotency and pluripotency are the cellular basis for root regeneration in Platycladus orientalis cuttings, and the regeneration of adventitious roots is a key determinant for improving stem-cutting. However, the cellular basis and physiological regulation of adventitious root formation are still ambiguous. In this research, root primordia initiation and organogenesis were histologically observed, dynamic alterations in soluble proteins were monitored, and Tandem Mass Tag protein profiling during adventitious root development was carried out. It was explicitly shown that the root primordium primarily originated from undifferentiated xylem cells for indirect (callus) rooting and from dividing cells in the cortex for direct (cortex) rooting. During the entire process of adventitious root development, the activities of peroxidase (POD) and polyphenol oxidase (PPO) peaked, and the activity of indole acetic acid oxidase (IAAO) decreased during the prophase of adventitious root formation in both the direct and indirect rooting, suggesting the positive regulation of POD and PPO and the negative regulation of IAAO during adventitious root initiation. Compared with those of indirect rooting, the relatively greater activities of POD and PPO and lower activity of IAAO were related to direct rooting and the number of adventitious roots. A total of 4265 proteins were identified from the base of the cuttings, of which 343, 236 and 37 proteins were highly expressed before treatment, in root primordia induction to adventitious root formation, and adventitious root elongation stages, respectively. Through hierarchical cluster analysis, 23 peroxidase and endogenous hormone regulatory proteins were screened and obtained; these included 10 peroxidases, 1 auxin regulatory protein, 3 ABA regulatory proteins, 2 jasmonic acid regulatory proteins, and 3 gibberellin regulatory proteins, which were highly expressed during the late cutting period. Finally, a hypothetical model of the regulatory network of the differential proteins involved in adventitious root formation in P. orientalis was constructed.

Gene prediction of the causal relationship between immune cells and IgA nephropathy: A bidirectional Mendelian randomization study.

IgA nephropathy is the most common primary glomerular disease worldwide, with inflammation and autoimmune response mechanisms permeating the entire disease development process. The advancement of genome-wide association studies has enabled deeper understanding of the disease mechanisms and genetic susceptibility. Therefore, this study aims to explore the causal relationship between 731 immune cell types and the disease through Mendelian randomization (MR) analysis. This 2-sample MR study investigated bidirectional causal relationships using summary statistics for immune cells characteristics from the Genome-Wide Association Study (GWAS) catalog and IgA nephropathy from the FinnGen dataset. The study primarily utilized the Inverse Variance Weighted method for its main outcome. Additionally, the robustness of the results is further enhanced by analyses of heterogeneity, pleiotropy, and multiple sensitivity tests. After adjusting for false discovery rate (FDR), the study results revealed a bidirectional causal relationship between CD8 on terminally differentiated CD8+ T cells (OR = 0.77, 95% CI = 0.67-0.88, P = .0001) and CD4 on CD28+ CD4+ T cells (OR = 0.75, 95% CI = 0.64-0.87, P = .0001) with the risk of IgA nephropathy. CD64 on CD14+ CD16+ monocytes (OR = 0.66, 95% CI = 0.51-0.85, P = .0013) is considered a protective factor, while the percentages of CD8+ and CD8dim T cells (1.38, 95% CI = 1.17-1.63, P = .0002) in leukocytes are viewed as risk factors. This study employed genetic variation as an instrumental variable to explore the genetic association between immune cells and IgA nephropathy, aiming to offer new insights into early prevention and personalized treatment of the disease.

11.D. Scientific session: Participatory approaches and co-creation in complex interventions to reduce inequity in health

Abstract Health disparities are linked to social inequalities, necessitating health researchers to address the health gaps that exist between different social groups, particularly in the realm of health interventions. This workshop delves into the significance of co-creation processes and participatory approaches in complex health interventions to reduce social inequities in health outcomes. Through empirical cases illustrating co-creation processes in intervention research, the workshop aims to enlighten the audience on the continuum of participatory approaches. The MRC/NIH framework of complex interventions highlights the importance of engagement with relevant stakeholders throughout the research phases to enhance the likelihood of developing or identifying interventions that can positively impact health outcomes. However, the extent and nature of stakeholder involvement may vary depending on the specific context and stage of the research. While stakeholder involvement has been a longstanding focus, the concept of co-creation, where stakeholders actively participate in the entire process of intervention development, implementation, and evaluation, has emerged more recently. This shift from creating interventions for the target population to collaborating with them in the intervention’s creation has led to the emergence of more inclusive health interventions that are contextually relevant and tailored to the local settings where they are intended to create change. Yet, introducing participatory approaches in intervention research may raise new questions about decision-making and power dynamics. While interventions led solely by experts and researchers may overlook the empowerment and capacity-building potential among stakeholders and intervention sites, co-creation processes have the capacity to empower stakeholders and address health inequities effectively. However, challenges persist in ensuring equal collaboration between researchers and participants, especially in disadvantaged groups. This workshop aims to prompt reflection on ethical issues arising from traditional health intervention approaches and approaches to ensure participants’ ownership of interventions when they are adapted in new contexts. Moreover, this workshop will discuss the design of participatory interventions: What methods can we use to co-create? What stakeholders to involve? At what stages of the intervention should co-creation occur? The workshop program begins with an introductory presentation of participatory approaches and co-creation in health intervention research, with examples from a school-based intervention. Next, three empirical cases of co-creation processes introduced in interventions in different contexts are presented. Finally, the audience is invited to a take part in a discussion focusing on how participatory approaches among disadvantaged populations can potentially contribute to reduced social inequity in health. Key messages • The involvement of stakeholders in the development, implementation and evaluation of health interventions can take many forms, and what is meaningful for the specific context needs to be considered. • Co-creation approaches in health interventions empower participants and address unequal power dynamics between those who deliver and those who receive health interventions.

A Comparative Analysis of Automated Machine Learning Tools: A Use Case for Autism Spectrum Disorder Detection

Automated Machine Learning (AutoML) enhances productivity and efficiency by automating the entire process of machine learning model development, from data preprocessing to model deployment. These tools are accessible to users with varying levels of expertise and enable efficient, scalable, and accurate classification across different applications. This paper evaluates two popular AutoML tools, the Tree-Based Pipeline Optimization Tool (TPOT) version 0.10.2 and Konstanz Information Miner (KNIME) version 5.2.5, comparing their performance in a classification task. Specifically, this work analyzes autism spectrum disorder (ASD) detection in toddlers as a use case. The dataset for ASD detection was collected from various rehabilitation centers in Pakistan. TPOT and KNIME were applied to the ASD dataset, with TPOT achieving an accuracy of 85.23% and KNIME achieving 83.89%. Evaluation metrics such as precision, recall, and F1-score validated the reliability of the models. After selecting the best models with optimal accuracy, the most important features for ASD detection were identified using these AutoML tools. The tools optimized the feature selection process and significantly reduced diagnosis time. This study demonstrates the potential of AutoML tools and feature selection techniques to improve early ASD detection and outcomes for affected children and their families.

Emotional and Psychological Safety in Healthcare Digitalization: A Design Ethnographic Study.

Emotional and psychological safety is important during the use of digital technologies in healthcare. We aimed to gain comprehensive insight into needs, influencing factors and outcomes in the context of perceived safety and digital technologies in healthcare. We employed a participatory, design ethnographic research approach with 16 participants in 10 use cases. The methods included in an iterative process were, think-aloud, guideline-based interviews, process mapping, storyboard creation, and photo documentation. A qualitative, primarily inductive data analysis and synthesis was performed. Perceived safety is influenced by various factors and unmet needs. Increased perceived safety can positively support the use of digital technologies, whereas low perceived safety can limit or even hinder its use. The needs of the different target groups should be considered throughout the entire process of digital technology development and healthcare provision to support their implementation. These findings support further research by providing specific aspects of emotional and psychological safety regarding target groups, settings, and ages and those with different levels of affinity for digital technologies.

Artificial Intelligence Supporting Early Automotive Engineering Processes

Abstract Due to progressive increase of complexity, the automotive industry is subject to constantly changing trends varying from the introduction of greener products and components to the deployment of technological advances in development and engineering processes. In relation to both, sustainable automotive products as well as the deployment of technological advances, the integration of AI (Artificial Intelligence) approaches in combination with virtual products in automotive development and engineering processes is of great importance. In combination with knowledge-based CAx (Computer-Aided engineering), the integration of AI approaches delivers an enormous potential to enhance automotive development processes. In addition to process optimizations, the integration of various AI approaches considers sustainability (e.g., optimization of component geometries and materials, reduction of emissions over the entire life cycle, CO2 reduction through improved development) and economical aspects (e.g., resources savings throughout the entire development process, time and cost savings through earlier error detection, avoidance of unnecessary process steps). The present approach deals with the integration of AI and knowledge-based engineering methods in the early phases of automotive development and engineering processes. Furthermore, the paper points to the time, cost and resources reduction potential, leading to earlier market entries and a greener industry. Finally, the paper demonstrates the integration of AI technologies into industrial development and engineering processes based on selected application scenarios.

Negative association of atmospheric pollutants with semen quality: A cross-sectional study in Taiyuan, China

BackgroundIn recent decades, the quality of male semen has decreased worldwide. Air pollution has been linked to lower semen quality in several studies. However, the effects of atmospheric pollutants on different semen characteristics have not always been consistent. The aim of this study was to investigate the association between the Air Quality Index (AQI) and six atmospheric pollutants (PM2.5, PM10, SO2, NO2, CO, and O3), semen quality, and their key exposure window periods. MethodsThis study included 1711 semen samples collected at the reproductive clinics of the First Affiliated Hospital of Shanxi Medical University in Taiyuan, Shanxi, China, from October 10, 2021, to September 30, 2022. We evaluated the association of AQI and six atmospheric pollutants with semen quality parameters throughout sperm development and three key exposure windows in men using single-pollutant models, double-pollutant models, and subgroup analyses of semen quality-eligible groups. ResultsBoth the single-pollutant model and subgroup analyses showed that PM, CO, and O3 levels were negatively correlated with total and progressive motility. At 70–90 d before semen collection, CO exposure and semen volume (β =-1.341, 95 % CI: −1.805, −0.877, P <0.001), total motility (β =-2.593, 95 % CI: −3.425, −1.761, P <0.001), and progressive motility (β =-4.658, 95 % CI: −5.556, −3.760, P <0.001) were negatively correlated. At 0–9 days before semen collection, CO was negatively correlated with normal morphology (β =-3.403, 95 % CI: −5.099, −1.708, P <0.001). Additionally, the AQI was adversely associated with total and progressive motility in subgroup analyses of the semen quality-eligible groups. ConclusionsDuring the entire sperm development process, multiple air pollutants were determined to have an adverse correlation with semen quality parameters. AQI was significant marker for the combined effects of various atmospheric pollutants on male reproductive health.

HAIGEN: Towards Human-AI Collaboration for Facilitating Creativity and Style Generation in Fashion Design

The process of fashion design usually involves sketching, refining, and coloring, with designers drawing inspiration from various images to fuel their creative endeavors. However, conventional image search methods often yield irrelevant results, impeding the design process. Moreover, creating and coloring sketches can be time-consuming and demanding, acting as a bottleneck in the design workflow. In this work, we introduce HAIGEN (Human-AI Collaboration for GENeration), an efficient fashion design system for Human-AI collaboration developed to aid designers. Specifically, HAIGEN consists of four modules. T2IM, located in the cloud, generates reference inspiration images directly from text prompts. With three other modules situated locally, the I2SM batch generates the image material library into a certain designer-style sketch material library. The SRM recommends similar sketches in the generated library to designers for further refinement, and the STM colors the refined sketch according to the styles of inspiration images. Through our system, any designer can perform local personalized fine-tuning and leverage the powerful generation capabilities of large models in the cloud, streamlining the entire design development process. Given that our approach integrates both cloud and local model deployment schemes, it effectively safeguards design privacy by avoiding the need to upload personalized data from local designers. We validated the effectiveness of each module through extensive qualitative and quantitative experiments. User surveys also confirmed that HAIGEN offers significant advantages in design efficiency, positioning it as a new generation of aid-tool for designers.

The Dawn of a New Pharmaceutical Epoch: Can AI and Robotics Reshape Drug Formulation?

Over the last four decades, pharmaceutical companies' expenditures on research and development have increased 51-fold. During this same time, clinical success rates for new drugs have remained unchanged at about 10 percent, predominantly due to lack of efficacy and/or safety concerns. This persistent problem underscores the need to innovate across the entire drug development process, particularly in drug formulation, which is often deprioritized and under-resourced.

Integrated Application of Risk Management Techniques in Developing an Analysis Method for Traditional Chinese Medicine: A Case Study of a Percolation Solution for Xiaochaihu Capsules

Risk management should run through the entire process of method development, utilization, and maintenance. Based on the analytical quality by design (AQbD) concept, various integrated risk management techniques were used in this study to develop an analysis method for the percolation solution of Xiaochaihu capsules. During the development of the analysis method, risk assessment was conducted using an Ishikawa diagram and failure mode effects analysis, followed by method optimization using experimental design. The probability of nonconformance calculated via an exhaustive Monte Carlo method quantitatively characterized the risk magnitude of method parameter failures, leading to the establishment of a operable design region method based on risk magnitude. Validation experiments and robustness tests of the data were utilized for model refinement and initial risk review. Methodological validation of the developed method was performed, and control strategies for the analysis method were presented through a decision tree. Stability experiments demonstrated that the samples remained stable at 4 °C for 24 h. The average recovery rate fell between 98.8% and 105%, with relative standard deviations ranging from 2.73% to 4.48%. The results showed that the established analysis method exhibited robustness. This analysis method can simultaneously determine the contents of uridine, adenine, 5-hydroxymethylfurfural, and guanosine. This method can also be employed for process control during percolation. This study integrated various risk management techniques to develop and maintain the analysis method, and this approach can potentially be extended to other analytical methods.

Salmonella Typhimurium alters galactitol metabolism under ciprofloxacin treatment to balance resistance and virulence.

Treatment of antibiotic resistance can significantly benefit from a deeper understanding of the interactions between drugs and genetics. The physiological responses and genetic mechanisms in antibiotic-exposed bacteria are not well understood. Traditional resistance studies, often retrospective, fail to capture the entire resistance development process and typically exhibit unpredictable dynamics. To explore how clinical isolates of S. Typhimurium respond to ciprofloxacin, we analyzed their adaptive responses. We found that S. Typhimurium RamA-mediated regulation disrupts microbial metabolism under ciprofloxacin exposure, affecting genes in the galactitol metabolic pathways. This disruption facilitates adaptive responses to drug therapy and enhances the efficiency of intracellular survival. A more comprehensive and integrated understanding of these physiological and genetic changes is crucial for improving treatment outcomes.

QUALITY ASSESSMENT OF THE SOFTWARE DEVELOPMENT PROCESS OF AN IT COMPANY BASED ON THE USE OF THE UTILITY FUNCTION

The paper considers the software development process as an object of research, which is a poorly structured system. A description of such systems is given in the form of general characteristics, which include: difficulties in building an analytical model; incompleteness, inaccuracy, unreliability and uncertainty of information; benchmarks required for assessing weakly structured systems are often absent; uniqueness of the decision-making process; dynamic nature of models of poorly structured systems, etc. In this paper, the quality assessment of the software development process is considered based on maturity model standards, which can have continuous and discrete variants. The continuous version assesses the quality of the individual focus areas and processes of the maturity models. For this purpose, a discrete point scale of the first type is used, when the assessment is carried out according to an objective criterion. The quality assessment of individual focus areas and processes characterizes the local criteria for assessing the quality of the entire software development process. Therefore, the task is to form some kind of integral quality assessment on their basis. One of the options for solving this problem is a discrete maturity model, where the scale for assessing the entire software development process has five gradations called maturity levels. Starting from the second level, each gradation is characterized by a set of focus areas with corresponding levels of capability. The availability of such a scale allows not only assessing the quality of the entire software development process, but also solving the task of planning to improve its quality. But first, it is necessary to analyse such a scale from the point of view of its balance, namely, that the distances on the scale between the gradations are approximately equal. Therefore, the paper analyses the existing scales that can be proposed for expert assessment for the quality of the software development process. Their construction can be realized on the basis of a utility function using the local criteria of maturity models formalized in this paper. For this purpose, a fundamental property of systems is used. Namely, the dependence of the utility (efficiency) of a complex system on the invested resources over the life cycle interval, which usually takes the form of a logistic curve. Further research will be devoted to using this fact to build a balanced scale for assessing the entire software development process based on maturity models.

Exploration of Innovative Teaching Paths in the Classroom of “The Outline of Modern and Contemporary Chinese History”

The purpose of “The Outline of Modern and Contemporary Chinese History” is to help college students grasp the entire process of historical development, summarize experiences correctly, understand the national history and conditions, deeply understand why history and the people made the “four choices,” and stimulate their patriotism, national pride and self-confidence. This article is based on the reform of multimedia teaching methods and carries out the construction of the “Outline” course from six dimensions: improving teaching methods, strengthening teaching material construction, improving online teaching, extracurricular training mode, strengthening teacher team construction and reforming the examination system. Efforts are made to build this course into a high-quality course, making it a course that college students truly love and benefit from.

An Experimental Study of the Morphological Evolution of Rills on Slopes under Rainfall Action

Accurately monitoring the morphology and spatiotemporal evolution characteristics of the entire process of slope erosion rill development is essential to circumvent the limitations inherent in traditional methods that rely on average flow velocity for hydrodynamic parameter calculations. This study employs an environmental chamber and a self-developed slope erosion test device to perform erosion tests on slopes with varying gradients and rainfall intensities. By integrating the structure-from-motion (SfM) method, fixed grid coordinate method, and continuous camera combined with the dye tracer technique, the morphological indexes and hydrodynamic parameters of the entire rill development process are precisely computed. The main conclusions are as follows: The entire process of slope rill development can be divided into three distinct stages. The initial stage is characterized by the appearance of tiny rills with mild erosion. The middle stage involves severe transverse spreading erosion and longitudinal undercutting, resulting in diverse rill morphologies. The final stage is marked by the stabilization of morphological characteristics. The peak slope soil loss is observed during the middle stage of rill development. The most effective parameters for characterizing slope soil loss from the beginning to the end are the Reynolds number and flow shear stress, the Froude number and flow shear stress, and the Froude number during different periods. Throughout the development of rills, the flow velocity initially decreases and then gradually increases until it stabilizes. The morphological indexes, including rill density, dissected degree, inclination, and complexity, generally show an increasing trend. However, in the middle stage, the rate of increase slows down, followed by a sharp rise at certain points. The optimal hydraulic parameters for evaluating rill density across different slope gradients, which were found to be the Darcy–Weisbach drag coefficient and real-time flow velocity, for assessing rill dissected degree, complexity, and inclination, were the Reynolds number and flow power. Under varying rainfall intensities, the most effective hydraulic and kinetic parameters for evaluating rill density, dissected degree, and inclination were flow shear stress and Reynolds number; for assessing rill complexity, the Reynolds number and flow power were used. The findings of this research enhance the accuracy of hydrodynamic parameter calculations in rill erosion tests, enable precise prediction of rill development trends on slopes, and offer innovative approaches for real-time dynamic monitoring of rill morphology and characteristics. These advancements are of significant importance for soil and water conservation and sustainability.

Metabolomic profiles and differential metabolites of volatile components in Citrus aurantium Changshan-huyou pericarp during different growth and development stages

Citrus fruits possess a distinctive aroma and flavor, with Citrus aurantium Changshan-huyou (CACH) standing out due to their considerable edible and medicinal value. However, the volatile components (VOCs) in the CACH pericarp (CP) remain underexplored. In this study, gas chromatography–mass spectrometry (GC–MS) was utilized to qualitatively analyze VOCs in 27 CP samples across different growth stages. A total of 544 VOCs were identified, including 91 terpenoids. The types, quantities and distributions of VOCs were conducted. Detailed discussions on the major terpenoids in CP were also presented. A metabolomics approach combining multivariate statistical analysis with univariate analysis was employed for screening the differential metabolites. The study provides comprehensive insights into the VOCs in CP and citrus plants. Moreover, it delivers the first in-depth analysis of differential metabolites in CP throughout the entire CACH growth and development process, laying a foundation for ongoing research and development of the VOCs in CP.

P-209 Development and characterization of human uterine myometrium in prenatal stage: insights from a single-cell atlas and three-dimensional imaging

Abstract Study question What’s the timepoint of human uterine myometrial formation and how dose it develop in the prenatal stage. Summary answer This study identified that the myometrium layer initially forms at postconceptional week (PCW) 11, ESR1+, IGFBP5, POSTIN may play pivotal role in myometrial development. What is known already Using immunohistochemistry staining, previous studies observed a faint expression of α-SMA in the outer mesenchyme at approximately 8-9 weeks of gestation and showed the early development of muscular layer in the uterine wall, and the expression of α-SMA becomes more prominent in the uterine corpus and cervix by 18 weeks, suggesting the maturation of smooth muscle cells may commence from 18 weeks of gestation. ESR-1 gene encodes estrogen receptor alpha, which plays a pivotal role in menstruation, pregnancy, and parturition. In adult female myometrium, estrogen performs functions in proliferation before term and contractile at term. Study design, size, duration This is an observational study. To investigate when exactly the smooth-muscle tissue appears during uterine development, we have collected 14 uterine tissue samples from 14 aborted fetuses across seven gestational time points from PCW 8 to PCW 15. Participants/materials, setting, methods Uterus from aborted fetuses from PCW8- PCW15 cover the fusion of Mullerian tube and development of Myometrium. The 10X genomics scRNA-seq and immune labeling were performed. In addition, we have integrated scRNA-seq data from adult uterine tissue for comparison. Main results and the role of chance We provide a single cell atlas of the human developing uterus, and we observed that smooth-muscle cells (SMCs), identified by ACTA2, TAGLN, MYH11, exhibited a significant increase in abundance starting from PCW 11, but weaker signals of SMCs were also detected at earlier stages. Using the iDISCO technique with continuous α-SMA labeling to examine the localization of α-SMA in the uterus between PCW10- PCW12, we observed the entire process of myometrial development in 3-D level. We utilized an oblique section of 3-D reconstruction uterus and extended it to 150 μm. Subsequently, apparent signals became visible in the middle of the uterine wall and the uterine fundus. Collectively, these findings suggest that the anatomical myometrium layer initially forms at PCW 11. We used the CytoTRACE to assess the capacity of differentiation of adult and fetal SMCs, SMCs come from fetus appeal a significant grander potential. Specifically, fetal uterine SMCs showed higher expression of ESR1, IGFBP5, and POSTN, while adult uterine SMCs exhibited higher expression of PDGFRB and IGFBP7. These results suggested ESR1+ SMCs could be the precursor of SMCs and SMCs in fetal uterus could be in a primary state. Limitations, reasons for caution While our study did not capture later stages of development or postnatal changes. Future studies are needed to understand the full spectrum of myometrial development. It is also important to note that the findings in this study are specific to human and may not directly translate to other species. Wider implications of the findings Our study provides a basis for studying abnormal myometrial development and its association with various reproductive disorders, such as the abnormal uterine development in female fetus with Turner syndrome. Potential biomarkers or therapeutic targets for uterine malformations and abnormal uterine contractility may also of research interest. Trial registration number not applicable

The heterogeneity of erythroid cells: insight at the single-cell transcriptome level.

Erythroid cells, the most prevalent cell type in blood, are one of the earliest products and permeate through the entire process of hematopoietic development in the human body, the oxygen-transporting function of which is crucial for maintaining overall health and life support. Previous investigations into erythrocyte differentiation and development have primarily focused on population-level analyses, lacking the single-cell perspective essential for comprehending the intricate pathways of erythroid maturation, differentiation, and the encompassing cellular heterogeneity. The continuous optimization of single-cell transcriptome sequencing technology, or single-cell RNA sequencing (scRNA-seq), provides a powerful tool for life sciences research, which has a particular superiority in the identification of unprecedented cell subgroups, the analyzing of cellular heterogeneity, and the transcriptomic characteristics of individual cells. Over the past decade, remarkable strides have been taken in the realm of single-cell RNA sequencing technology, profoundly enhancing our understanding of erythroid cells. In this review, we systematically summarize the recent developments in single-cell transcriptome sequencing technology and emphasize their substantial impact on the study of erythroid cells, highlighting their contributions, including the exploration of functional heterogeneity within erythroid populations, the identification of novel erythrocyte subgroups, the tracking of different erythroid lineages, and the unveiling of mechanisms governing erythroid fate decisions. These findings not only invigorate erythroid cell research but also offer new perspectives on the management of diseases related to erythroid cells.

Impact Analysis of using Natural Language Processing and Large Language Model on Automated Correction of Systems Engineering Requirements

AbstractThe increasing complexity of Electronic Control Units (ECUs) in the Automotive Industry due to the integration of more sophisticated vehicle features has made the Systems Engineering (SE) application a necessity to define and implement efficient solutions. In this context, requirements emerge as a critical part of the communication between cross‐functional teams. Thus, the more complex systems become, the more requirements are needed to define them. However, lack of information, misalignment and ambiguity on requirements impact the entire development process, resulting in issues later, harder to be fixed. Some studies are being applied to evaluate techniques using Natural Language Processing (NLP) and how it can replace extensive peer reviews, identifying weaknesses in requirements earlier in the process, avoiding wasted time and large financial losses. Normally, NLP is combined with templates such as Easy Approach Requirements to Syntax (EARS), or other rule‐based techniques such as INCOSE's requirements writing best practice rules to define metrics and assess the compliance of the requirements syntax automatically. The focus of this work is to enhance the use of requirements syntax assessment algorithm by combining NLP techniques with Large Language Models (LLMs) to provide automatically corrected requirements.