Research Tool Research Articles

Assessing the metastatic potential of circulating tumor cells using an organ-on-chip model.

Metastatic lung cancer remains a leading cause of death worldwide, with its intricate metastatic cascade posing significant challenges to researchers and clinicians. Despite substantial progress in understanding this cascade, many aspects remain elusive. Microfluidic-based vasculature-on-chip models have emerged as powerful tools in cancer research, enabling the simulation of specific stages of tumor progression. In this study, we investigate the extravasation behaviors of A549 lung cancer cell subpopulations, revealing distinct differences based on their phenotypes. Our results show that holoclones, which exhibit an epithelial phenotype, do not undergo extravasation. In contrast, paraclones, characterized by a mesenchymal phenotype, demonstrate a notable capacity for extravasation. Furthermore, we observed that paraclones migrate significantly faster than holoclones within the microfluidic model. Importantly, we found that the depletion of vascular endothelial growth factor (VEGF) effectively inhibits the extravasation of paraclones. These findings highlight the utility of microfluidic-based models in replicating key aspects of the metastatic cascade. The insights gained from this study underscore the potential of these models to advance precision medicine by facilitating the assessment of patient-specific cancer cell dynamics and drug responses. This approach could lead to improved strategies for predicting metastatic risk and tailoring personalized cancer therapies, potentially involving the sampling of cancer cells from patients during tumor resection or biopsies.

Artifacts and Anomalies in Raman Spectroscopy: A Review on Origins and Correction Procedures

Raman spectroscopy, renowned for its unique ability to provide a molecular fingerprint, is an invaluable tool in industry and academic research. However, various constraints often hinder the measurement process, leading to artifacts and anomalies that can significantly affect spectral measurements. This review begins by thoroughly discussing the origins and impacts of these artifacts and anomalies stemming from instrumental, sampling, and sample-related factors. Following this, we present a comprehensive list and categorization of the existing correction procedures, including computational, experimental, and deep learning (DL) approaches. The review concludes by identifying the limitations of current procedures and discussing recent advancements and breakthroughs. This discussion highlights the potential of these advancements and provides a clear direction for future research to enhance correction procedures in Raman spectral analysis.

18F]FDG Brain Imaging Assessment in Preclinical Acute and Chronic Stress Models Using Statistical Parametric Mapping

Previous studies have used [18F]FDG as a biomarker for depression, anxiety, or chronic stress in cancer patients1,2. However, they lack specific brain patterns and assessment in preclinical models, limiting their translational potential. Hence, we set out to establish preclinical [18F]FDG brain PET as a method to evaluate stress in vivo. Lean C57BL/6J mice were exposed to acute and chronic cooling, restraint, and surgical stress. Obese mice were on a high-fat diet for eight weeks and exposed to cooling and restraint stress. [18F]FDG was injected into fasted animals under brief isoflurane anesthesia via the tail vein. Following a 60-minute distribution phase during acute stress or one day after stress exposure (chronic), animals underwent static μPET/CT scans (Siemens® Inveon). Brain images were spatially normalized, and intensity normalization was performed using proportional and histogram-based scaling3 with PMOD (V. 3.8). Normalized images were analyzed using a brain atlas (M. Mirrione) to extract regional SUV and statistical parametric mapping (SPM12) for voxel-wise comparison. Overall, in lean mice, total brain [18F]FDG-uptake was significantly reduced across all stress models except for acute restraint, whereas total uptake was unaffected in obese mice. Subsequent brain atlas analysis of lean mice revealed changes in regional uptake for all stressors except for chronic cooling, while obese mice were affected by only acute and chronic restraint. Using SPM, we validated stress-specific hypo- and hypermetabolic areas in the brains of lean mice upon acute cooling, acute and chronic restraint, and surgery and in obese mice after chronic restraint (see Figure). We also identified overlapping areas in the brain stem and hypothalamus region following acute cooling and surgery, possibly due to post-surgical hypothermia. Our findings show that static [18F]FDG-imaging is a valuable tool for noninvasively assessing brain activity in a multitude of stress models and a suitable translational tool for stress research in mice. Please click on the 'PDF' for the full abstract!

New and Modified Homotopy Perturbation Methods for Addressing Burger’s Non-linear Equation

Objectives: The aim of this study is to find a novel solution procedure for solving a fluid dynamical problem, especially to solve two-dimensional coupled Burger’s non-linear equation. Methods: New and Modified homotopy perturbation techniques are used to solve the two-dimensional coupled Burger’s equation. The methods intend to make homotopy perturbation method a more robust and trustworthy tool for fluid dynamics researchers by addressing convergence concerns, improving solution accuracy and allowing it to handle a broader range of problems. Findings: The solution for two-dimensional coupled Burger’s non-linear equation is obtained by constructing homotopies. Numerical results thus obtained are compared with the exact solution. The obtained results and the exact solutions to the stated problem are closely related. Novelty: The New and Modified Homotopy Perturbation Method proposed in this paper are effective mathematical tools for getting an exact solution to the coupled Burgers' equations. It is also a potential approach for solving various linear and nonlinear partial differential equations. Keywords: Perturbation, Homotopy, New homotopy perturbation, Modified homotopy perturbation, Two­dimension, Linear, Coupled burgers' equation

Software System SPDIAL

Spiroid gears are direct competitors to traditional technical solutions - worm and hypoid gears; and they have proven themselves in a large number of specific technical applications, providing greater strength, smaller size, and easier production. However, their design process is much less subject to being typical and standard and to resulting in simple engineering techniques. In fact, effective solutions can be obtained only by actually researching the space of parameters. An indispensable tool for this is a computer-aided design and research system. The paper presents the summary of such a system SPDIAL+ for worm-type gears (worm, QN-gears, spiroid) developed at Izhevsk scientific school in the field of gears and gearboxes. In particular, the possibilities of designing a conjugated gear, choice of gearwheel cutting parameters, analysis of gears under the action of errors and loads are considered. The role and results of applying the program as the main tool for research, design and innovations like new varieties of gears, their new properties in specific applications, new methods and techniques of tooth machining are shown.

A Macroscopic Exploration of the Ideoscape on Exosomes for Bone Regeneration

Background: Exosomes, nanoscale extracellular vesicles, play a crucial role in tissue physiology and regeneration. This study uses infometric techniques to explore the structure of exosome-based tissue and bone regeneration research. Methods: We applied BERTopic, an advanced topic modeling algorithm, to a comprehensive corpus of the scientific literature on exosomes and tissue regeneration, identifying key themes such as stem cell studies, tissue healing, and regenerative applications, with orthopedics and dentistry emerging as dominant subfields. To further investigate the ‘ideoscape’, i.e., the conceptual landscape that maps how ideas, methods, and themes are interconnected across the field, we extracted significant concepts from abstracts using GPT 3.5 turbo and created knowledge graphs. Results: Our analysis revealed rapid growth in the field of dental stem cell regeneration, which has outpaced other bone regeneration topics by twofold. This analysis highlighted central themes such as periodontal stem cells and their cellular processes—proliferation, migration, and differentiation—along with their clinical applications. Our approach provided a clear visualization of the field’s intellectual structure, showing how emerging topics are interconnected. Our findings offer a comprehensive view of the evolving trends in exosome-based bone regeneration, revealing not only the most active research areas but also gaps and opportunities for further investigation. Conclusions: This study exemplifies the utility of combining topic modeling with knowledge graph creation to map research trends, offering a flexible and largely automated tool for researchers to explore the vast bodies of literature and guide future research directions.

The Immigration‐Related Political Ideology Scale: Development, factor structure, and validity evidence

AbstractThe aim of the present study was to develop and validate the Immigration‐Related Political Ideology Scale (IRPIS), designed to measure diverse political perspectives on immigration among US voters. The IRPIS was developed following a series of focus groups and refined through a systematic item‐generation process involving expert panels. The scale was validated using a two‐step factor analysis with a nationally representative sample of 1292 US voters, divided into exploratory (n = 632) and confirmatory (n = 660) sub‐samples. The exploratory factor analysis identified seven distinct factors: conservative views, welcoming attitudes, world regions, flexibility, assimilationist expectations, multicultural expectations, and undocumented immigrant rights. Confirmatory factor analysis provided an acceptable fit (CFI = .901; RMSEA = .050), and the scale demonstrated high internal consistency (Cronbach's alphas ranging from .89 to .97). Inter‐factor correlations varied, with strong links observed among welcoming attitudes, flexibility, and multicultural expectations (r’s > .80), confirming a polarization between liberal and conservative stances on immigration. These findings suggest that the IRPIS is the first scale specifically designed to assess immigration‐related political orientations in a polarized US context. This tool has important implications for political strategy and policymaking, especially in the context of rising immigration rates and political polarization.Public Significance StatementThe present study validates the Immigration‐Related Political Ideology Scale (IRPIS) using a nationally representative sample of 1292 US voters. Immigration remains a highly polarizing issue, with deeply entrenched ideological differences influencing policy preferences and public discourse. Exploratory factor analysis identified seven distinct factors and confirmed a clear polarization between liberal and conservative views on immigration. The IRPIS represents a critical tool for research, political strategy, and policymaking.

Advanced breast diffusion-weighted imaging: what are the next steps? A proposal from the EUSOBI International Breast Diffusion-weighted Imaging working group.

This study by the EUSOBI International Breast Diffusion-weighted Imaging (DWI) working group aimed to evaluate the current and future applications of advanced DWI in breast imaging. A literature search and a comprehensive survey of EUSOBI members to explore the clinical use and potential of advanced DWI techniques and a literature search were involved. Advanced DWI approaches such as intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI), and diffusion tensor imaging (DTI) were assessed for their current status and challenges in clinical implementation. Although a literature search revealed an increasing number of publications and growing academic interest in advanced DWI, the survey revealed limited adoption of advanced DWI techniques among EUSOBI members, with 32% using IVIM models, 17% using non-Gaussian diffusion techniques for kurtosis analysis, and only 8% using DTI. A variety of DWI techniques are used, with IVIM being the most popular, but less than half use it, suggesting that the study identified a gap between the potential benefits of advanced DWI and its actual use in clinical practice. The findings highlight the need for further research, standardization and simplification to transition advanced DWI from a research tool to regular practice in breast imaging. The study concludes with guidelines and recommendations for future research directions and clinical implementation, emphasizing the importance of interdisciplinary collaboration in this field to improve breast cancer diagnosis and treatment. Advanced DWI in breast imaging, while currently in limited clinical use, offers promising improvements in diagnosis, staging, and treatment monitoring, highlighting the need for standardized protocols, accessible software, and collaborative approaches to promote its broader integration into routine clinical practice. Increasing number of publications on advanced DWI over the last decade indicates growing research interest. EUSOBI survey shows that advanced DWI is used primarily in research, not extensively in clinical practice. More research and standardization are needed to integrate advanced DWI into routine breast imaging practice.

Leveraging Technological Research Methodologies to Improve Employee Performance in Organizations

The effective management of human resources in organizations is imperative for attaining optimum performance and eventual profitability. The increasingly competitive marketplace has compelled organizations to work towards attaining operational excellence, which heavily relies on the performance of its human resources. The adoption of cutting-edge research techniques has emerged as a pivotal strategy as organizations endeavour to achieve operational excellence. However, human resource managers have had to grapple with various challenges associated with the management of employee data generated and managed through digital platforms and tools, including issues such as data safety and security. This paper explores the intersection between tech-enhanced, advanced research methodologies and employee performance that is geared toward sustainable economic growth at the organizational level. Notably, the paper examines big data analytics as a tech-enhanced research tool leveraged by organizations in a bid to understand their human resource and the best way they can be managed to ensure high performance, which translates into high productivity and competitiveness in their respective sector or industries. In particular, this paper conducts a comprehensive review of recent extant literature regarding the adoption of big data analytics, which has revolutionized how organizations collect and analyze vast amounts of data, thereby enhancing their decision-making process. The reviewed literature focused on human resource aspects such as recruitment and selection; employee development; employee performance management and compensation structures. The review revealed that big data has emerged as a critical tool for managing large volumes of data that are acquired via numerous digital tools as organizations grapple with the integration of technology into their human resource practices. The technologies have helped human resource managers with these structured and unstructured data, they gain insight that in turn, guides their decision-making processes and improves the overall operational efficiency. The review outlines the transformative potential of integrating big data tools with human resource management and recommends that organizations should invest in these tools to enhance their recruitment and selection, employee development, performance measurement and upgrade their compensation structure.

Bibliometric and visual analysis of single-cell multiomics in neurodegenerative disease arrest studies.

Neurodegenerative diseases are progressive disorders that severely diminish the quality of life of patients. However, research on neurodegenerative diseases needs to be refined and deepened. Single-cell polyomics is a technique for obtaining transcriptomic, proteomic, and other information from a single cell. In recent years, the heat of single-cell multiomics as an emerging research tool for brain science has gradually increased. Therefore, the aim of this study was to analyze the current status and trends of studies related to the application of single-cell multiomics in neurodegenerative diseases through bibliometrics. A total of 596 publications were included in the bibliometric analysis. Between 2015 and 2022, the number of publications increased annually, with the total number of citations increasing significantly, exhibiting the fastest rate of growth between 2019 and 2022. The country/region collaboration map shows that the United States has the most publications and cumulative citations, and that China and the United States have the most collaborations. The institutions that produced the greatest number of articles were Harvard Medical School, Skupin, Alexander, and Wiendl. Among the authors, Heinz had the highest output. Mathys, H accumulated the most citations and was the authoritative author in the field. The journal Nature Communications has published the most literature in this field. A keyword analysis reveals that neurodegenerative diseases and lesions (e.g., Alzheimer's disease, amyloid beta) are the core and foundation of the field. Conversely, single-cell multiomics related research (e.g., single-cell RNA sequencing, bioinformatics) and brain nerve cells (e.g., microglia, astrocytes, neural stem cells) are the hot frontiers of this specialty. Among the references, the article "Single-cell transcriptomic analysis of Alzheimer's disease" is the most frequently cited (1,146 citations), and the article "Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq" was the most cited article in the field. The objective of this study is to employ bibliometric methods to visualize studies related to single-cell multiomics in neurodegenerative diseases. This will enable us to summarize the current state of research and to reveal key trends and emerging hotspots in the field.

Efficient Algorithm for Generating Optimal Inequality Candidates for MILP Modeling of Boolean Functions

Mixed-Integer Linear Programming (MILP) modeling has become an important tool for both the analysis and the design of symmetric cryptographic primitives. The bit-wise modeling of their nonlinear components, especially the S-boxes, is of particular interest since it allows more informative analysis compared to word-oriented models focusing on counting active S-boxes. At the same time, the size of these models, especially in terms of the number of required inequalities, tends to significantly influence and ultimately limit the applicability of this method to real-world ciphers, especially for larger number of rounds. It is therefore of great cryptographic significance to study optimal linear inequality descriptions for Boolean functions. The pioneering works of Abdelkhalek et al. (FSE 2017), Boura and Coggia (FSE 2020) and Li and Sun (FSE 2023) provided various heuristic techniques for this computationally hard problem, decomposing it into two algorithmic steps, coined Problem 1 and Problem 2, with the latter being identical to the well-known NP-hard set cover problem, for which there are many heuristic and exact algorithms in the literature. In this paper, we introduce a novel and efficient branch-and-bound algorithm for generating all minimal, non-redundant candidate inequalities that satisfy a given Boolean function, therefore solving Problem 1 in an optimal manner without relying on heuristics. We furthermore prove that our algorithm correctly computes optimal solutions. Using a number of dedicated optimizations, it provides significantly improved runtimes compared to previous approaches and allows the optimal modeling of the difference distribution tables (DDT) and linear approximation tables (LAT) of many practically used S-boxes. The source code for our algorithm is publicly available as a tool for researchers and practitioners in symmetric cryptography.

Innovative Processes in the Sanatorium and Resort Sector of the Republic of Belarus

INTRODUCTION. The introduction of innovative processes in sanatorium-resort activities helps to approach the individual treatment of a citizen’s disease or preventive measures aimed at reducing the incidence of the population in a sanatorium-resort area, simplify the functioning of health resorts and their provision of high-quality services for sanatorium treatment and rehabilitation. AIM. To determine the level of public awareness about sanatorium-resort organizations in the Republic of Belarus and to propose an algorithm for introducing innovative processes into their activities. MATERIALS AND METHODS. The survey method was used in the study. The research tool was a questionnaire. The representativeness of the sample was ensured by calculating the required number of respondents, taking into account the population of the regions of the Republic of Belarus (923 people) account the power of the study at the level (1 – β) = 80 %. A mobile application has been developed in the Visual Studio Code environment to improve the activities and optimize organizational processes in the health resort in order to raise public awareness, based on a study conducted in the health resorts of the republic to assess the quality of services provided for sanatorium treatment and wellness. RESULTS AND DISCUSSION. To identify the factors influencing the choice of a health resort by the population, cluster analysis, showed the closest correlation between the factors “doctors’ recommendations” and “medical services” influencing the choice of a health resort. The distribution of health resort awareness assessment in the regional context varied from 10.5 % (Grodno region) to 17.7 % (Minsk). The following are proposed: a system for informing the population and an algorithm for meeting the needs of the population in treatment and rehabilitation, the participation of information in sanatorium and resort activities. A selective assessment of the quality of spa treatment and wellness services in health resorts was carried out. A mobile application has been developed. CONCLUSION. The level of awareness of the population of the Republic of Belarus about the activities of sanatorium-resort organizations was assessed using a survey method — regional differences were identified. A model of the public information system and an algorithm for meeting the needs of the population and health improvement, the participation of information in sanatorium and resort activities are presented. A mobile application has been developed for sanatorium-resort organizations.

The PROMETHEE-GAIA: A multi-criteria decision-making method for identifying best conservation agricultural practices

In a first, we used Preference Ranking Organization Method and Geometrical Analysis for Interactive Aid (PROMETHEE-GAIA) tool in agricultural research for identifying the best management decision with weakening factors for cultivation of mustard following rice under fifteen regimes of conservation agriculture (CA) practices, because of its versatility, simplicity and usefulness. We created the regime of CA practices based on energy spent for raising the crop combining different tillage practices viz., reduced tillage and zero tillage, and quantity of crop residues and fertilizer application. The performance of the CA regimes was evaluated over the conventional ones using conflicting criteria (31) related to soil, agronomy, plant protection, energy use and economics. With the PROMETHEE-II method, we found zero tillage with 100 % residue plus 75 % NPK (ZERO3) as the best performing alternative, while GAIA analysis identified ZERO3 and zero tillage with 50 % residue plus 100 % NPK (ZERO4). On integration, PROMETHEE-GAIA helped the decision makers to segregate the effects of the criteria on the outcome creating a scope for maneuvering the weak links for optimizing the performance of mustard crop under different CA-regimes. The tool has a huge potential for use in multi-factorial agricultural research.

Animal models in neuroscience with alternative approaches: Evolutionary, biomedical, and ethical perspectives.

Animal models have been a crucial tool in neuroscience research for decades, providing insights into the biomedical and evolutionary mechanisms of the nervous system, disease, and behavior. However, their use has raised concerns on several ethical, clinical, and scientific considerations. The welfare of animals and the 3R principles (replacement, reduction, refinement) are the focus of the ethical concerns, targeting the importance of reducing the stress and suffering of these models. Several laws and guidelines are applied and developed to protect animal rights during experimenting. Concurrently, in the clinic and biomedical fields, discussions on the relevance of animal model findings on human organisms have increased. Latest data suggest that in a considerable amount of time the animal model results are not translatable in humans, costing time and money. Alternative methods, such as invitro (cell culture, microscopy, organoids, and micro physiological systems) techniques and in silico (computational) modeling, have emerged as potential replacements for animal models, providing more accurate data in a minimized cost. By adopting alternative methods and promoting ethical considerations in research practices, we can achieve the 3R goals while upholding our responsibility to both humans and other animals. Our goal is to present a thorough review of animal models used in neuroscience from the biomedical, evolutionary, and ethical perspectives. The novelty of this research lies in integrating diverse points of views to provide an understanding of the advantages and disadvantages of animal models in neuroscience and in discussing potential alternative methods.

ModelHamiltonian: A Python-scriptable library for generating 0-, 1-, and 2-electron integrals.

ModelHamiltonian is a free, open source, and cross-platform Python library designed to express model Hamiltonians, including spin-based Hamiltonians (Heisenberg and Ising models) and occupation-based Hamiltonians (Pariser-Parr-Pople, Hubbard, and Hückel models) in terms of 1- and 2-electron integrals, so that these systems can be easily treated by traditional quantum chemistry software programs. ModelHamiltonian was originally intended to facilitate the testing of new electronic structure methods using HORTON but emerged as a stand-alone research tool that we recognize has wide utility, even in an educational context. ModelHamiltonian is written in Python and adheres to modern principles of software development, including comprehensive documentation, extensive testing, continuous integration/delivery protocols, and package management. While we anticipate that most users will use ModelHamiltonian as a Python library, we include a graphical user interface so that models can be built without programming, based on connectivity/parameters inferred from, for example, a SMILES string. We also include an interface to ChatGPT so that users can specify a Hamiltonian in plain language (without learning ModelHamiltonian's vocabulary and syntax). This article marks the official release of the ModelHamiltonian library, showcasing its functionality and scope.

PyCI: A Python-scriptable library for arbitrary determinant CI.

PyCI is a free and open-source Python library for setting up and running arbitrary determinant-driven configuration interaction (CI) computations, as well as their generalizations to cases where the coefficients of the determinant are nonlinear functions of optimizable parameters. PyCI also includes functionality for computing the residual correlation energy, along with the ability to compute spin-polarized one- and two-electron (transition) reduced density matrices. PyCI was originally intended to replace the abinitio quantum chemistry functionality in the HORTON library but emerged as a standalone research tool, primarily intended to aid in method development, while maintaining high performance so that it is suitable for practical calculations. To this end, PyCI is written in Python, adopting principles of modern software development, including comprehensive documentation, extensive testing, continuous integration/delivery protocols, and package management. Computationally intensive steps, notably operations related to generating Slater determinants and computing their expectation values, are delegated to low-level C++ code. This article marks the official release of the PyCI library, showcasing its functionality and scope.

Morphological Processes in English and Sanskrit: A Cross-Linguistic Study

Morphology is the sub-discipline of linguistics. It is the study of morphemes and their arrangement in forming words. The present study attempts to identify and compare the morphological processes in English and Sanskrit. This study was based on qualitative methods and the selected documents of both languages were used as the research tools for this study. The results reveal that the English suffixes such as -age, -al,-ation,-ism, -ment, -ant, -ent, -er, and Sanskrit suffixes such as -ya/-eya, -man, -na, -an, -a, -ti -a, -ana, -ah, -ti, -tri, -aka, -man, are nominal derivational suffixes. The plural morpheme -s, and genitive morpheme -'s are English nominal inflectional morphemes whereas there are twenty-one inflectional suffixes (sup) (su, au, jas) in Sanskrit. Similarly, English verbal inflectional morphemes are -ed, -en, and -ing whereas there are eighteen verbal inflectional suffixes (tin) (tip, tas, jhi,) in Sanskrit. Furthermore, there are compound nouns (breakfast), verbs (fine tune), adjectives (open-ended), and adverbs (uprightly) in English. In contrast, there are only two special sub-class of exocentric compounds in Sanskrit, namely numerative (dvigu), and adverbials (avyayībhāva). As a comparative study, it can help identify the root knowledge of both languages as languages are related to each other.

Weighted gene co-expression network analysis and identification of ginsenoside biosynthesis candidate genes for ginseng adventitious roots under MeJA treatment.

Ginseng (Panax ginseng) is an herb with a long history and a wide range of applications. Ginsenoside is one of the most representative and active ginseng compounds, with various pharmacological effects. Therefore, the development of bioreactors using methyl jasmonate (MeJA) as an inducer for targeted ginsenoside production is of great commercial value. Combined with transcriptomic research tools, screenings to obtain candidate genes involved in ginsenoside biosynthesis are crucial for future discoveries about the molecular mechanism of MeJA-regulated ginsenoside biosynthesis. In our study, the ginsenoside content of ginseng adventitious roots treated with MeJA at different times was analyzed. Transcriptome analysis was performed to investigate the effects of MeJA on changes in ginsenoside content in ginseng adventitious roots. The MeJA could significantly increase changes in the content of pro-ginsenodiol ginsenosides as well as pro-triol ginsenosides Rg3, Re, and Rf in ginseng adventitious roots. Differential gene expression analysis showed that a total of 14,009 differentially expressed genes were obtained from the screening of the present study. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that differentially expressed genes were mainly enriched under GO terms in response to stimuli, metabolic processes, and the regulation of biological processes, with significant annotation to the metabolic terms of terpenoids and polyketides. Two expression modules of genes highly related to ginsenoside biosynthesis were obtained via WGCNA. Our study provides a reference system for the targeted ginsenoside production using MeJA as an inducer, and also provides genetic and gene resources for subsequently validating genes related to the regulation of ginsenoside biosynthesis using weighted gene co-expression network analysis (WGCNA).

A taxonomic guide to diffusion MRI tractography visualization tools.

Visualizing neuroimaging data is a key step in evaluating data quality, interpreting results, and communicating findings. This survey focuses on diffusion MRI tractography, which has been widely used in both research and clinical domains within the neuroimaging community. With an increasing number of tractography tools and software, navigating this landscape poses a challenge, especially for newcomers. A systematic exploration of a diverse range of features is proposed across 27 research tools, delving into their main purpose and examining the presence or absence of prevalent visualization and interactive techniques. The findings are structured within a proposed taxonomy, providing a comprehensive overview. Insights derived from this analysis will help (novice) researchers, clinicians, and developers in identifying knowledge gaps and navigating the landscape of tractography visualization tools.

State-of-the-Art in Skin Fluorescent Photography for Cosmetic and Skincare Research: From Molecular Spectra to AI Image Analysis.

Autofluorescence is a remarkable property of human skin. It can be excited by UV and observed in the dark using special detection systems. The method of fluorescence photography (FP) is an effective non-invasive tool for skin assessment. It involves image capturing by a camera the emission of light quanta from fluorophore molecules in the skin. It serves as a useful tool for cosmetic and skincare research, especially for the detection of pathological skin states, like acne, psoriasis, etc. To the best of our knowledge, there is currently no comprehensive review that fully describes the application and physical principles of FP over the past five years. The current review covers various aspects of the skin FP method from its biophysical basis and the main fluorescent molecules of the skin to its potential applications and the principles of FP recording and analysis. We pay particular attention to recently reported works on the automatic analysis of FP based on artificial intelligence (AI). Thus, we argue that FP is a rapidly evolving technology with a wide range of potential applications. We propose potential directions of the development of this method, including new AI algorithms for the analysis and expanding the range of applications.