Search

The article announces the winners of Development's Outstanding Paper Prize for papers published in 2023. The winning papers focus on developmental and stem cell biology. One paper demonstrates the potential of genome engineering techniques and pharmacological upregulation of the Wnt signaling pathway in treating frizzled 2-associated congenital malformations in mouse models. Another paper explores how Frizzled2 receives WntA signaling during butterfly wing pattern formation, providing insights into the complexity and evolution of signaling pathways. The article also mentions a study on the development of primordial germ cell-like cells using stem cell technologies. Additionally, the article discusses a study on the molecular functioning of WntA in butterfly wing color patterning, which involved collaboration between international teams and faced challenges in rearing and injecting butterflies in the lab. The researchers plan to further investigate butterfly color patterns. [Extracted from the article]

The journal 'Life' has released a Special Issue that showcases a range of studies in the field of biological research. The articles in this issue cover topics such as genetics, disease biomarkers, musculoskeletal health, and therapeutic interventions. One study focuses on the link between genetic variations in the heat shock factor 1 gene and an increased risk of type 2 diabetes, particularly in overweight and obese females. Another study explores the use of biomarkers for diagnosing and treating osteoarthritis, with the goal of improving patient outcomes. The issue also addresses the impact of obesity on musculoskeletal health and the role of exercise in mitigating chemotherapy-induced peripheral neuropathy. Additionally, the intersection of autoimmune diseases and parasitic infections is examined, revealing potential therapeutic insights. The issue also highlights research on sepsis-induced coagulopathy, platelet-derived extracellular vesicles, chondrocyte biology, and innovative treatments for dermatitis associated with immunoglobulin G4-related disease. The articles emphasize the need for interdisciplinary collaboration in addressing complex health and disease challenges. Overall, this Special Issue contributes to our understanding of human health and offers potential strategies for improvement. [Extracted from the article]

[Display omitted] • Designing materials using rice-leaf-mimetic paper as a substrate. • Engineering rice-leaf-mimetic paper with hydrochromic dyes or biolubricants. • Processes based on mass-producible papermaking unit operations. Biomimetics has revolutionized materials innovation by drawing inspiration from biological systems. Cellulosic paper, a sustainable biomaterial, offers immense potential for biomimetic research. Paper-based biomimetic materials, which replicate the structures and functionalities of natural materials by utilizing the cellulosic fiber network as a substrate or skeleton, provide customizable functional properties, thereby enabling advanced applications. Here, we introduce rice-leaf-mimetic paper, inspired by the leaf's microstructure and biowax-based cuticle. This paper exhibits self-cleaning superhydrophobicity and showcases possibilities for additional functionalities, including antibacterial properties and food preservation, as evidenced in our prior research. We propose its utilization as a substrate for rewritable and biolubricant-infused surfaces. These somehow distinct surfaces draw inspiration from the chemistry and microstructure of the rice leaf. Although incorporating hydrochromic dyes enables rewritability, controlling the retention time of colors remains challenging. The biowaxy structure of the paper prolongs color maintenance by inhibiting water evaporation. Moreover, the hierarchically rough microstructure of rice-leaf-mimetic paper effectively stores biolubricant molecules, reducing friction and enhancing slipperiness. Surface microcavity ratio analysis provides insights into the paper's biolubricant-storability. The surface engineering of rice-leaf-mimetic paper using hydrochromic dyes or biolubricants holds potential for designing novel materials. Unveiling the vast potential of easily scalable papermaking processes alongside biomimetics and surface engineering opens new avenues for materials innovation. [ABSTRACT FROM AUTHOR]

Adverse Outcome Pathways (AOPs) have been proposed to facilitate mechanistic understanding of interactions of chemicals/materials with biological systems. Each AOP starts with a molecular initiating event (MIE) and possibly ends with adverse outcome(s) (AOs) via a series of key events (KEs). So far, the interaction of engineered nanomaterials (ENMs) with biomolecules, biomembranes, cells, and biological structures, in general, is not yet fully elucidated. There is also a huge lack of information on which AOPs are ENMs-relevant or -specific, despite numerous published data on toxicological endpoints they trigger, such as oxidative stress and inflammation. We propose to integrate related data and knowledge recently collected. Our approach combines the annotation of nanomaterials and their MIEs with ontology annotation to demonstrate how we can then query AOPs and biological pathway information for these materials. We conclude that a FAIR (Findable, Accessible, Interoperable, Reusable) representation of the ENM-MIE knowledge simplifies integration with other knowledge. Scientific contribution: This study introduces a new database linking nanomaterial stressors to the first known MIE or KE. Second, it presents a reproducible workflow to analyze and summarize this knowledge. Third, this work extends the use of semantic web technologies to the field of nanoinformatics and nanosafety. [ABSTRACT FROM AUTHOR]

Nanoplastics (NPs, <1 µm) pose greater risks due to their increased absorption rates in biological systems. In this study, we investigated the release of NPs from paper cups and microwavable food containers coated with low-density polyethylene (LDPE) and polylactic acid (PLA). For disposable paper cups, we found that LDPE-coated cups released up to 26-fold more NPs (maximum 1.9 × 107 per cup) than PLA-coated ones. The NPs release from LDPE-coated cups was increased at high temperatures above 80 °C, and further increased by physical agitation. However, negligible NP release was observed when the inner coating thickness exceeded 1 mm. For microwavable food containers, those with PLA coatings were more susceptible to the effects of microwave. Depending on the cooking time, we noticed a significant difference (up to 40000 times) in the number of released NPs between LDPE and PLA coatings. Additionally, higher microwave power level led to an increase of NPs, even with constant total energy input. Considering the release of NP, PLA coatings for disposable paper cups and LDPE coatings for microwavable food containers seem more suitable. Furthermore, our results suggest that multi-use cups significantly reduce NPs release due to their material thickness, making them a safer alternative to disposable ones. [Display omitted] • Generation of up to 1.9 × 107 nanoplastics in LDPE-coated cup. • Vulnerability to nanoplastics for PLA-coated microwavable food containers. • Negligible nanoplastics detected in reusable products (thickness > 1 mm). [ABSTRACT FROM AUTHOR]

This document is a summary of a special issue of the journal Applied Sciences titled "Biomechanics and Human Motion Analysis." The special issue includes twenty papers that cover a wide range of topics related to the analysis of human motion and biomechanics. The studies involve both healthy subjects and patients with joint diseases, and they utilize musculoskeletal modeling to support their investigations. The papers were contributed by researchers from different countries and continents, highlighting the global nature of this field of study. The special issue aims to promote the integration of biomechanical analysis and instruments into motion analysis and to demonstrate the potential of modern techniques in addressing complex biomechanical problems in human body dynamics. The contributions cover a range of topics related to biomechanics and motion analysis, including the assessment of patellofemoral pain, shoulder kinematics in overhead sporting motions, tibiofemoral kinematics, military crawl locomotion, instability during stepping, upper body posture recognition, optimal control strategies during obstacle crossing, scoring of human body-balance ability, center of mass motion during gait, kinematic analysis of basketball shots, hidden Markov models in movement pattern identification, smart phone-based motion capture, gait analysis in personalized high tibial osteotomy, walking without an anterior cruciate ligament, crouch gait analysis, spine, pelvis, and hip kinematics, hamstring torque measurements, modeling musculoskeletal dynamics during gait, comparison of joint axis estimation approaches, and balanced foot dorsiflex [Extracted from the article]

Lab on a Chip is a journal published by the Royal Society of Chemistry that focuses on devices and applications at the micro- and nanoscale. The current issue includes various articles and papers on topics such as membrane-based microfluidic systems, modelling the innate immune system, sample preparation and detection methods in point-of-care devices, and more. The journal aims to connect the world with the chemical sciences and reinvests its profits back into the chemistry community. [Extracted from the article]

New discoveries and knowledge are summarized in thousands of published papers per year per scientific domain, making it incomprehensible for scientists to account for all available knowledge relevant for their studies. In this paper, we present ACCORDION (ACCelerating and Optimizing model RecommenDatIONs), a novel methodology and an expert system that retrieves and selects relevant knowledge from literature and databases to recommend models with correct structure and accurate behavior, enabling mechanistic explanations and predictions, and advancing understanding. ACCORDION introduces an approach that integrates knowledge retrieval, graph algorithms, clustering, simulation, and formal analysis. Here, we focus on biological systems, although the proposed methodology is applicable in other domains. We used ACCORDION in nine benchmark case studies and compared its performance with other previously published tools. We show that ACCORDION is: comprehensive, retrieving relevant knowledge from a range of literature sources through machine reading engines; very effective, reducing the error of the initial baseline model by more than 80%, recommending models that closely recapitulate desired behavior, and outperforming previously published tools; selective, recommending only the most relevant, contextspecific, and useful subset (15%-20%) of candidate knowledge in literature; diverse, accounting for several distinct criteria to recommend more than one solution, thus enabling alternative explanations or intervention directions. [ABSTRACT FROM AUTHOR]

A new bio-inspired flapping wing rotor (FWR) configuration combining the high lift of a flapping wing (FW) with the vertical take-off and landing characteristics of a rotor has proved its concept feasibility in the last decade and can be used for reconnaissance and aerial photography missions in complex and confined environments. During high-frequency flapping motion, the leading and trailing edge vortices of the flapping wing rotor alternately shed and form vortex sheets in the wake, resulting in periodic lift and a highly unsteady flow field. In the past, unsteady potential flow aerodynamic methods were derived based on a two-dimensional (2D) airfoil and extended to three dimensions by the blade element theory, neglecting the spanwise evolution effect of the vortices. In this paper, an unsteady vortex ring method (UVRM) model was developed based on two-dimensional (3D) potential flow theory for the aerodynamic calculation of a flapping wing rotor, which takes into account the spanwise evolution of shedding vortices at the leading and trailing edges of the flapping wing. The calculation results of the UVRM for several typical cases were compared with those of the computational fluid dynamics (CFD) method to verify the reliability of the UVRM. Then, fluid–structure interaction analysis for the flapping wing rotor was conducted based on the UVRM model, and the phase difference between twist deformation and flapping angle was found to be approximately 90°. In addition, the effects of wing chordwise and spanwise deformation on the aerodynamic performance of the flapping rotor were investigated based on the UVRM. The results show that the wing chordwise deformation has a positive effect on the mean lift coefficient of the flapping rotor. The wing spanwise deformation has a negative effect on the mean lift coefficient of the flapping rotor. The unsteady vortex ring method model proposed in this paper provides a solution to obtain the aerodynamic force of the flapping wing rotor in an efficient and accurate way. [ABSTRACT FROM AUTHOR]

When students think of evolution, they might imagine T. rex, or perhaps an abiotic scene of sizzling electrical storms and harsh reducing atmospheres, an Earth that looks like a lunar landscape. Natural selection automatically elicits responses that include "survival of the fittest," and "descent with modification," and with these historical biological catch phrases, one conjures up images of large animals battling it out on the Mesozoic plane. Rarely do teachers or students apply these same ideas to cancer and the evolution of somatic cells, which have accrued mutations and epigenetic imprinting and relentlessly survive and proliferate. Our questions in this paper include the following: Can cancer become an important teaching model for students to explore fundamental hypotheses about evolutionary process? Can the multi- step somatic cancer model encourage visualizations that enable students to revisit and reenter previous primary concepts in general biology such as the cell, mitosis, chromosomes, genetic diversity, ecological diversity, immune function, and of course evolution, continually integrating their biology knowledge into process and pattern knowledge? Can the somatic cancer model expose similar patterns and protagonists, linking Darwinian observations of the natural world to our body? And, can the cancer clone model excite critical thinking and student hypotheses about what cancer is as a biological process? Does this visually simple model assist students in recognizing patterns, connecting their biological curriculum dots into a more coherent learning experience? These biological dynamics and intercepting aptitudes of cells are amplified through the cancer model and can help shape the way biology students begin to appreciate the interrelatedness of all biological systems while they continue to explore pivotal points of biological fuzziness, such as the microbiome, limitations of models, and the complex coordination of genomic networks required for the function of even a single cell and the realization of phenotypes. In this paper we use clonal evolution of cancer as a model experience for students to recreate how a single, non-germline cell appears to shadow the classic pattern of natural selection in body cells that have gone awry. With authentic STEAM activities students can easily crossover and revisit previous biological topics and the ubiquitous nature of natural selection as seen in the example of somatic cells that result in a metastasizing tumor, giving students insight into natural selection's accommodating and tractable patterns throughout the planet. [ABSTRACT FROM AUTHOR]

First Person is a series of interviews with the first authors of a selection of papers published in Journal of Cell Science, helping researchers promote themselves alongside their papers. Anne Rosfelter is first author on 'Reduction of cortical pulling at mitotic entry facilitates aster centration', published in JCS. Anne conducted the research described in this article while a PhD student in Alex McDougall's lab at Laboratoire Biologie du De'veloppement (LBDV), Villefranche-sur-Mer, France. She is now a post-doctoral researcher in the lab of Yu-Chiun Wang at the RIKEN Center for Biosystems Dynamics Research (BDR), Kobe, Japan, investigating the spatial organization of cells and tissue through studying the cytoskeleton. [ABSTRACT FROM AUTHOR]

For orthopedic implants, biocompatibility is essential to ensure that they integrate with biological systems without causing negative reactions or impairing tissue function. Orthopedic implants must be cellularly acceptable to interact with neighboring tissues, including muscle, cartilage, and bone. Implant materials should not cause cytotoxic reactions or inflammatory responses that might hinder healing or result in long-term inflammation. Progress in surface engineering and biomaterials science is discovering implants that work well with the body, benefiting patients in the long run. The biocompatibility of orthopedic implants, their interaction with surrounding tissues, and the potential for biological problems are all important aspects of their design and operation. Orthopedic implants must be successful and long-lasting to ensure biocompatibility, promote osseointegration (osteointegration), avoid biofilm development, and consider the patient's biological environment. This paper discusses biocompatibility in orthopedic implants, its advancements, and challenges. [ABSTRACT FROM AUTHOR]

This paper presents an in-depth analysis of an excitable membrane of a biological system by proposing a novel approach that the cells of the excitable membrane can be modeled as the networks of memristors. We provide compelling evidence from the Chay neuron model that the state-independent mixed ion channel is a nonlinear resistor, while the state-dependent voltage-sensitive potassium ion channel and calcium-sensitive potassium ion channel function as generic memristors from the perspective of electrical circuit theory. The mechanisms that give rise to periodic oscillation, aperiodic (chaotic) oscillation, spikes, and bursting in an excitable cell are also analyzed via a small-signal model, a pole-zero diagram of admittance functions, local activity, the edge of chaos, and the Hopf bifurcation theorem. It is also proved that the zeros of the admittance functions are equivalent to the eigen values of the Jacobian matrix, and the presence of the positive real parts of the eigen values between the two bifurcation points lead to the generation of complicated electrical signals in an excitable membrane. The innovative concepts outlined in this paper pave the way for a deeper understanding of the dynamic behavior of excitable cells, offering potent tools for simulating and exploring the fundamental characteristics of biological neurons. [ABSTRACT FROM AUTHOR]

With the ongoing amendment of the EU legislation on the treatment of urban wastewater, stricter requirements for the removal of pollutants are expected, which calls for the need for innovative wastewater treatment technologies. Biological systems are still the first choice. A survey of typical bioreactors applied in wastewater treatment is presented. The wastewater treatment objective, biochemical environment, and microbial growth are selected as the main criteria for the classification of these bioreactors. Hydraulic and kinetic aspects are considered, along with the advantages and drawbacks of these bioreactors regarding the selection of the appropriate type of reactor; as well, details regarding the operation of reactors are mentioned. The aim of this paper is to provide operators and designers with a brief overview of the selected traditional and advanced processes, reactors, and technologies for nutrient removal from municipal wastewater. The possibilities and limitations in complying with more strict effluent standards are also discussed. Methods of nutrient recovery are added value. From the evaluation of the published papers, we determine that the currently applied traditional methods for nutrient removal have the potential to also convey the expected stricter limits. [ABSTRACT FROM AUTHOR]

This document discusses the challenges faced by plants in arid and semi-arid regions due to harsh environmental conditions such as limited rainfall, high temperatures, and poor soil nutrition. The document highlights the importance of understanding plant adaptation strategies to extreme environments in order to protect and manage local vegetation and ecosystems. The document presents research papers that cover various aspects of plant adaptation in arid regions, including water uptake mechanisms, physiological and biochemical adaptations, plant-soil microbial interactions, and ecosystem services. The research findings provide insights into how plants in arid regions adapt to environmental changes and maintain ecological balance. However, further research is needed to fully understand the processes and mechanisms of plant adaptation in extreme environments. [Extracted from the article]

The SIAM Journal on Applied Mathematics has published a special section on the life sciences, highlighting the use of mathematical models and analysis in understanding biological systems. The section showcases a range of topics, including population dynamics, genetics, neuroscience, epidemics, and various physiological systems. The papers in the section focus on providing biological insights or suggesting novel experiments, and they incorporate practical issues, data-driven modeling, and uncertainty quantification. The special section reflects the growing interest and activity in the fields of mathematical and computational biology, which have been driven by real-world applications in health, medicine, and bioengineering. [Extracted from the article]

First Person is a series of interviews with the first authors of a selection of papers published in Journal of Cell Science, helping researchers promote themselves alongside their papers. Vidur Sabharwal is first author on 'F-box protein FBXB-65 regulates anterograde transport of the kinesin-3 motor UNC-104 through a PTM near its cargo-binding PH domain', published in JCS. Vidur conducted the research described in this article while a PhD student in Sandhya P. Koushika's lab at the Tata Institute of Fundamental Research, Mumbai, India. He is now a postdoc in the lab of Abdel El Manira at Karolinska Institutet, Stockholm, Sweden, where he works to understand and describe how complex observations in biological systems emerge from simple underlying mechanisms. [ABSTRACT FROM AUTHOR]

The liver and kidney are the major detoxifying organs in the human body and play an important role in pharmacokinetics. Drug-induced hepatotoxicity and nephrotoxicity can cause irreversible damage to the liver and kidney and are a major cause of drug failure in later stages. Both animal models and conventional cell culture have a number of limitations, such as animal ethics and gene mismatching and there is an urgent need to develop a new drug toxicity evaluation approach. In this paper, a 3D liver–kidney on a chip with a biomimicking circulating system (LKOCBCS) was constructed to obtain kidney and liver models in vitro for drug safety evaluation. LKOCBCS, which has a parallel circulating system mimicking biological circulation, consists of 3D biomimetic tissue of liver lobules similar to that of the human liver constructed by 3D bioprinting and renal proximal tubule barriers fabricated by ultrafast laser assisted etching. The proposed LKOCBCS facilitates the communication between the liver and the kidney, including the exchange of nutrients, compounds, and metabolites. The results revealed that the glucose concentration and cell metabolism stabilized after 7 days. A dynamically repeated low-dose administration of cyclosporine A (CsA) was fed to the system, and hepatotoxicity and nephrotoxicity were observed on day 3 according to the changes in toxicity markers. The high levels of drug induced biomarkers expressed in LKOCBCS indicate that this system is more sensitive than the monoculture liver chip and it is highly potential in replacing animal models for effective drug toxicity screening. [ABSTRACT FROM AUTHOR]

Genetic oscillators have been widely used in modeling key processes of biological systems, especially cell cycles and circadian rhythms. In particular, repressilatory genetic oscillators have been employed in modeling the dynamics of mRNA and protein interactions with transcriptional and translational feedback loops at the molecular level. In addition, synchronization of these oscillators is crucial for understanding the underlying mechanisms of the associated biological processes. In this paper, models of fractional-order genetic oscillators and their coupling are established, where the aspects of time delay, coupling strength, noise, and stability are all taken into consideration. Communication in the proposed coupling model is based on quorum sensing. The synchronization of the fractional-order repressilator model has been examined through simulations which show three main findings. Firstly, the synchronization of the fractional-order repressilator model can be optimized through coupling weight selection. Secondly, the synchronization can be enhanced by increasing the fractional order and decreasing the time delay and the noise intensity. Finally, transitions between the states of the fractional-order repressilatory oscillator can be achieved through varying the fractional order. The simulation results verify the biological relevance of the genetic oscillator models, and their potential for explaining the underlying mechanisms of the associated biological processes. [ABSTRACT FROM AUTHOR]

This paper focuses on using a novel approach to assess the statistical variability of management data from an aerobic thermophilic biological plant (AWTP) utilizing a fluidized bed biological reactor. A proper statistical characterization of full-scale thermophilic biological systems, in fact, may inform process optimization in the light of a future automation of treatment plants. We present a case study that spans the period from 2018 to 2023 and encompasses various high-strength aqueous waste (AW) in continuous mode. Key aspects of the proposed analytical approach include: (i) utilizing advanced descriptive statistics, such as violin graphs, to depict the variability of monitored parameters over five years; (ii) conducting correlation analyses (Spearman and Pearson correlation matrices) specifically focusing on nitrogenous forms within the AW; (iii) applying multivariate statistical analysis to assess the correlation between pollutants released and the plant's energy and oxygen consumption; and (iv) reconstructing parameter trends by considering periodic and random components, thus enhancing the understanding of the system's behavior over time. The findings presented in this paper offer valuable insights into the performance and optimization of AWTPs, potentially leading to a proper planning of the loads and consequent feeding of the plants. If properly enacted, our approach may provide a significant contribution to the field of aqueous waste management. [ABSTRACT FROM AUTHOR]

A biomaterial is a component that has been developed to interact with biological systems for therapeutic or diagnostic purposes in medicine. Tissue engineering, bioprinting, and regenerative medicine are just a few of the increasingly complex fields where biomaterials are being used. These applications frequently require difficult or even paradoxical combinations of biomaterial qualities that cannot be satisfied by traditional biomaterials. Many novel proposals have been introduced over the past 10 years to make biomaterials self-healing, opening up fresh possibilities for enhancing the functionality of conventional biomaterials. Thus, the selfhealing composite that will be discussed in the present article is one such biomaterial that comes into play. Self-healing composites are composites that automatically heal in the area where damage has occurred. This material draws inspiration from the way our body heals itself through regenerative processes, and these self-healing composites utilise various additional healing methods as well. This paper outlines the various aspects of self-healing composites and their types, with a focus on capsule-based and vascular self-healing systems. As a complement to previous reviews, this paper provides insights into the diverse self-repairing concepts proposed so far, as well as compares the study of healing mechanisms and manufacturing approaches for the assembly of capsule and vascular networks. The current concept of self-healing polymers provides advanced avenues for secure, longer-lasting, and more durable products and parts across a wide range of industries, such as veneers, electronics, transport, and energy. [ABSTRACT FROM AUTHOR]

The integration of biology and spacefaring has led to the development of three interrelated fields: Astrobiology, Bioastronautics, and Space Bioprocess Engineering. Astrobiology is concerned with the study of the origin, evolution, distribution, and future of life in the universe, while Bioastronautics focuses on the effects of spaceflight on biological systems, including human physiology and psychology. Space Bioprocess Engineering, on the other hand, deals with the design, deployment, and management of biotechnology for human exploration. This paper highlights the unique contributions of each field and outlines opportunities for biologists to engage in these exciting avenues of research. By providing a clear overview of the major fields of biology and spacefaring, this paper serves as a valuable resource for scientists and researchers interested in exploring the integration of these disciplines. [ABSTRACT FROM AUTHOR]

Copyright of Cuadernos del Centro de Estudios de Diseño y Comunicación is the property of Cuadernos del Centro de Estudios de Diseno y Comunicacion and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Construction of co-occurrence networks in metagenomic data often employs correlation to infer pairwise relationships between microbes. However, biological systems are complex and often display qualities non-linear in nature. Therefore, the reliance on correlation alone may overlook important relationships and fail to capture the full breadth of intricacies presented in underlying interaction networks. It is of interest to incorporate metrics that are not only robust in detecting linear relationships, but non-linear ones as well. Results: In this paper, we explore the use of various mutual information (MI) estimation approaches for quantifying pairwise relationships in biological data and compare their performances against two traditional measures–Pearson's correlation coefficient, r, and Spearman's rank correlation coefficient, ρ. Metrics are tested on both simulated data designed to mimic pairwise relationships that may be found in ecological systems and real data from a previous study on C. diff infection. The results demonstrate that, in the case of asymmetric relationships, mutual information estimators can provide better detection ability than Pearson's or Spearman's correlation coefficients. Specifically, we find that these estimators have elevated performances in the detection of exploitative relationships, demonstrating the potential benefit of including them in future metagenomic studies. Conclusions: Mutual information (MI) can uncover complex pairwise relationships in biological data that may be missed by traditional measures of association. The inclusion of such relationships when constructing co-occurrence networks can result in a more comprehensive analysis than the use of correlation alone. [ABSTRACT FROM AUTHOR]

Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease with a complex pathological mechanism involving autoimmune response, local inflammation and bone destruction. Metabolic pathways play an important role in immune-related diseases and their immune responses. The pathogenesis of rheumatoid arthritis may be related to its metabolic dysregulation. Moreover, histological techniques, including genomics, transcriptomics, proteomics and metabolomics, provide powerful tools for comprehensive analysis of molecular changes in biological systems. The present study explores the molecular and metabolic mechanisms of RA, emphasizing the central role of metabolic dysregulation in the RA disease process and highlighting the complexity of metabolic pathways, particularly metabolic remodeling in synovial tissues and its association with cytokine-mediated inflammation. This paper reveals the potential of histological techniques in identifying metabolically relevant therapeutic targets in RA; specifically, we summarize the genetic basis of RA and the dysregulated metabolic pathways, and explore their functional significance in the context of immune cell activation and differentiation. This study demonstrates the critical role of histological techniques in decoding the complex metabolic network of RA and discusses the integration of histological data with other types of biological data. [ABSTRACT FROM AUTHOR]

Analyzing protein–protein interaction (PPI) networks using machine learning and deep learning algorithms, alongside centrality measures, holds paramount importance in understanding complex biological systems. These advanced computational techniques enable the extraction of valuable insights from intricate network structures, shedding light on the functional relationships between proteins. By leveraging AI-driven approaches, researchers can uncover key regulatory mechanisms, identify critical nodes within the network and predict novel protein interactions with high accuracy. Ultimately, this integration of computational methodologies enhances our ability to comprehend the dynamic behavior of biological systems at a molecular level, paving the way for advancements in drug discovery, disease understanding and personalized medicine. This review paper starts by outlining various popular available PPI network databases and network centrality calculation tools. A thorough classification of various centrality measures has been identified. It primarily delves into the centrality-driven discoveries within PPI networks in biological systems and suggests using edge centrality measures and a hybrid version of node and edge centrality measures in machine learning algorithms and deep learning algorithms to predict hidden knowledge much more effectively. The review paper begins by outlining various popular available PPI network databases and network centrality calculation tools. A thorough classification of various centrality measures has been identified. A detailed view of various findings of different types of centrality measures in PPI networks is presented. [ABSTRACT FROM AUTHOR]

Reaction Systems (RSs) are a computational framework inspired by biochemical mechanisms. An RS defines a finite set of reactions over a finite set of entities (molecules, proteins, etc). Starting from an initial set of entities (the initial state), a computation is performed by applying all reactions to a state in order to produce the following state, giving rise to a sequence of sets of entities. RSs have shown to be a general computational framework whose application ranges from the modeling of biological phenomena to molecular chemistry and computer science. In this paper, we contribute to research on the application of RSs for modeling biological systems. We consider the problem of modeling hemostasis, for which several models have been defined, starting from the 1960s. Previous models are based on sets of ordinary differential equations, while we develop a discrete model in RSs for pathways of the secondary hemostasis. Then, we implement our model in BioReSolve, a computational framework for RSs that we have previously defined which provides tools for the specification and verification of properties. By using the tools in BioReSolve we derive important observations on the model behaviour for hemostasis, and in particular, we study the role of three important inhibitors, verifying that their presence or absence leads to phenomena such as thrombophilia, or thromboembolism, or excessive coagulation, etc. We can also study computationally the causality relations between the molecules involved in the reactions showing which entities play a fundamental role, thus contributing to the design of more effective and specialized drugs. Our work can hence help to show how to model complex biological systems in RSs and derive computationally and biologically relevant properties of the systems. [ABSTRACT FROM AUTHOR]

Dynamical system models typically involve numerous input parameters whose "effects" and orthogonality need to be quantified through sensitivity analysis, to identify inputs contributing the greatest uncertainty. Whilst prior art has compared total-order estimators' role in recovering "true" effects, assessing their ability to recover robust parameter orthogonality for use in identifiability metrics has not been investigated. In this paper, we perform: (i) an assessment using a different class of numerical models representing the cardiovascular system, (ii) a wider evaluation of sampling methodologies and their interactions with estimators, (iii) an investigation of the consequences of permuting estimators and sampling methodologies on input parameter orthogonality, (iv) a study of sample convergence through resampling, and (v) an assessment of whether positive outcomes are sustained when model input dimensionality increases. Our results indicate that Jansen or Janon estimators display efficient convergence with minimum uncertainty when coupled with Sobol and the lattice rule sampling methods, making them prime choices for calculating parameter orthogonality and influence. This study reveals that global sensitivity analysis is convergence driven. Unconverged indices are subject to error and therefore the true influence or orthogonality of the input parameters are not recovered. This investigation importantly clarifies the interactions of the estimator and the sampling methodology by reducing the associated ambiguities, defining novel practices for modelling in the life sciences. Author summary: In order to gain new insight into a biological system, one often uses mathematical models to predict possible responses from the system. One vital step when using such models is to gain knowledge of the uncertainty associated with the model responses, for any input changes. Utilising two non-linear and stiff cardiovascular models as test cases, we investigate the effects of different choices made when quantifying the uncertainty of mathematical models. Leveraging efficient solving of the mathematical model, we show that in order to truly quantify the effects of inputs on a set of outputs, one must ensure converged estimates of the inputs' influence. Our detailed study provides a robust workflow of good modelling practice for biological systems, thus ensuring a true interpretation of the uncertainty associated with model inputs. [ABSTRACT FROM AUTHOR]

Bioactive compounds have drawn much attention according to their various health benefits. However, poor dissolvability, low stability and limited bioavailability largely narrow their applications. Although a variety of nontoxic ingredients have been rapidly developed as vehicles to deliver bioactive compounds in the last few years, most of them are non-bioactive. Pentacyclic triterpenoids, owing to their unique self-assembly and co-assembly behaviors and different physiological functions, can construct bioactive carriers due to their higher biodegradability, biocompatibility and lower toxicity. In this paper, the basic classification, biological activities and physicochemical properties of pentacyclic triterpenoids were summarized. Additionally, applications of self-assembled and co-assembled pentacyclic triterpenoids as bioactive delivery systems to load bioactive components and future research directions were discussed. This study emphasizes the potential of pentacyclic triterpenoids as bioactive delivery systems, offering a new perspective for constructing self- or co-assemblies for further synergetic biological applications. [ABSTRACT FROM AUTHOR]

Neurons within the pre-Bötzinger complex have been found experimentally to exhibit different firing activities. Mixed bursting is a special firing activities which has been found in neuron model and biological experiments. The traditional fast and slow dynamics analysis method encounters some difficulties in analyzing mixed bursting. Therefore, how to analyze the dynamic mechanism of mixed bursting generation and transition is a very important issue. In this paper, based on fast-slow decomposition, multiple timescale dynamics and one- and two-parameter bifurcation analysis, we investigate the mixed bursting and their transition mechanisms in pre-Bötzinger complex neurons. The results show that the generation mechanism of the mixed bursting is related to the multiple timescale of the neuron model. The results obtained in this paper is instructive for further understanding the dynamical behaviours and generation mechanisms of complex firing activities in biological nervous systems. [ABSTRACT FROM AUTHOR]

Computational biological models have proven to be an invaluable tool for understanding and predicting the behaviour of many biological systems. While it may not be too challenging for experienced researchers to construct such models from scratch, it is not a straightforward task for early stage researchers. Design patterns are well-known techniques widely applied in software engineering as they provide a set of typical solutions to common problems in software design. In this paper, we collect and discuss common patterns that are usually used during the construction and execution of computational biological models. We adopt Petri nets as a modelling language to provide a visual illustration of each pattern; however, the ideas presented in this paper can also be implemented using other modelling formalisms. We provide two case studies for illustration purposes and show how these models can be built up from the presented smaller modules. We hope that the ideas discussed in this paper will help many researchers in building their own future models. [ABSTRACT FROM AUTHOR]

The subject of this paper concerns with the bifurcation of limit cycles for a predator-prey model with small immigration. Since, in general, the biological systems are not isolated, taking into account immigration in the model becomes more realistic. In this context, we deal with a model with a Holling type Ⅰ function response and study, using averaging theory of second order, the Hopf bifurcation that can emerge under small perturbation of the biological parameters. [ABSTRACT FROM AUTHOR]

This paper presents a number of lessons learned while designing a proposed sterilization system for Mars Sample Return. This sterilization system is needed to inactivate any potentially hazardous Mars material on the exterior surface of the vessel containing sealed sample tubes filled with Mars rock cores, regolith and atmosphere. These returned samples would provide information on the geologic history of Mars, the evolution of its climate and the potential for ancient life. Mars Sample Return is categorized at Planetary Protection Category V Restricted Earth Return, so it is required to protect the Earth–Moon system from the biological impact of returning samples from Mars to Earth. This article reviews lessons learned in the development of a particular engineering implementation to support the protection of the Earth–Moon biosphere: the use of in situ ultraviolet LED illumination. The details of the biological efficacy of this approach or the policy-related impacts are outside of the scope of this manuscript. The lessons learned presented here include establishing design requirements for the system, the selection of a light source, optical design options, contamination control and approaches to thermal and power management. [ABSTRACT FROM AUTHOR]

Background: In bioinformatics, interactions are modelled as networks, based on graph models. Generally, these support a single-layer structure which incorporates a specific entity (i.e., node) and only one type of link (i.e., edge). However, real-world biological systems consisting of biological objects belonging to heterogeneous entities, and these operate and influence each other in multiple contexts, simultaneously. Usually, node similarities are investigated to assess the relatedness between biological objects in a network of interest, and node embeddings are widely used for studying novel interaction from a topological point of view. About that, the state-of-the-art presents several methods for evaluating the node similarity inside a given network, but methodologies able to evaluate similarities between pairs of nodes belonging to different networks are missing. The latter are crucial for studies that relate different biological networks, e.g., for Network Alignment or to evaluate the possible evolution of the interactions of a little-known network on the basis of a well-known one. Existing methods are ineffective in evaluating nodes outside their structure, even more so in the context of multilayer networks, in which the topic still exploits approaches adapted from static networks. In this paper, we presented pyMulSim, a novel method for computing the pairwise similarities between nodes belonging to different multilayer networks. It uses a Graph Isomorphism Network (GIN) for the representative learning of node features, that uses for processing the embeddings and computing the similarities between the pairs of nodes of different multilayer networks. Results: Our experimentation investigated the performance of our method. Results show that our method effectively evaluates the similarities between the biological objects of a source multilayer network to a target one, based on the analysis of the node embeddings. Results have been also assessed for different noise levels, also through statistical significance analyses properly performed for this purpose. Conclusions: PyMulSim is a novel method for computing the pairwise similarities between nodes belonging to different multilayer networks, by using a GIN for learning node embeddings. It has been evaluated both in terms of performance and validity, reporting a high degree of reliability. [ABSTRACT FROM AUTHOR]

This paper forges a strong connection between two well known computational frameworks for representing biological systems, in order to facilitate the seamless transfer of techniques between them. Boolean networks are a well established formalism employed from biologists. They have been studied under different (synchronous and asynchronous) update semantics, enabling the observation and characterisation of distinct facets of system behaviour. Recently, a new semantics for Boolean networks has been proposed, called most permissive semantics, that enables a more faithful representation of biological phenomena. Reaction systems offer a streamlined formalism inspired by biochemical reactions in living cells. Reaction systems support a full range of analysis techniques that can help for gaining deeper insights into the underlying biological phenomena. Our goal is to leverage the available toolkit for predicting and comprehending the behaviour of reaction systems within the realm of Boolean networks. In this paper, we first extend the behaviour of reaction systems to several asynchronous semantics, including the most permissive one, and then we demonstrate that Boolean networks and reaction systems exhibit isomorphic behaviours under the synchronous, general/fully asynchronous and most permissive semantics. [ABSTRACT FROM AUTHOR]

In this paper, we delve into the intricate local dynamics at equilibria within a two-dimensional model of hepatitis C virus (HCV) alongside hepatocyte homeostasis. The study investigates the existence of bifurcation sets and conducts a comprehensive bifurcation analysis to elucidate the system's behavior under varying conditions. A significant focus lies on understanding how changes in parameters can lead to bifurcations, which are pivotal points where the qualitative behavior of the system undergoes fundamental transformations. Moreover, the paper introduces and employs hybrid control feedback and Ott–Grebogi–Yorke strategies as tools to manage and mitigate chaos inherent within the HCV model. This chaos arises due to the presence of flip and Neimark–Sacker bifurcations, which can induce erratic behavior in the system. Through the implementation of these control strategies, the study aims to stabilize the system and restore it to a more manageable and predictable state. Furthermore, to validate the theoretical findings and the efficacy of the proposed control strategies, extensive numerical simulations are conducted. These simulations serve as a means of confirming the theoretical predictions and provide insight into the practical implications of the proposed control methodologies. By combining theoretical analysis with computational simulations, the paper offers a comprehensive understanding of the dynamics of the HCV model and provides valuable insights into potential strategies for controlling and managing chaos in such complex biological systems. [ABSTRACT FROM AUTHOR]

In this paper, we introduce SonoNERFs, a novel approach that adapts Neural Radiance Fields (NeRFs) to model and understand the echolocation process in bats, focusing on the challenges posed by acoustic data interpretation without phase information. Leveraging insights from the field of optical NeRFs, our model, termed SonoNERF, represents the acoustic environment through Neural Reflectivity Fields. This model allows us to reconstruct three-dimensional scenes from echolocation data, obtained by simulating how bats perceive their surroundings through sound. By integrating concepts from biological echolocation and modern computational models, we demonstrate the SonoNERF's ability to predict echo spectrograms for unseen echolocation poses and effectively reconstruct a mesh-based and energy-based representation of complex scenes. Our work bridges a gap in understanding biological echolocation and proposes a methodological framework that provides a first-order model of how scene understanding might arise in echolocating animals. We demonstrate the efficacy of the SonoNERF model on three scenes of increasing complexity, including some biologically relevant prey–predator interactions. [ABSTRACT FROM AUTHOR]

Bioinspired structural color represents a burgeoning field that draws upon principles, strategies, and concepts derived from biological systems to inspire the design of novel technologies or products featuring reversible color changing mechanisms, with significant potential applications for camouflage, sensors, anticounterfeiting, etc. This mini-review focuses specifically on the research progress of bioinspired structural color in the realm of camouflage. Firstly, it discusses fundamental mechanisms of coloration in biological systems, encompassing pigmentation, structural coloration, fluorescence, and bioluminescence. Subsequently, it delineates three modulation strategies—namely, photonic crystals, film interference, and plasmonic modulation—that contribute to the development of bioinspired structural color materials or devices. Moreover, the review critically assesses the integration of bioinspired structural color materials with environmental contexts, with a particular emphasis on their application in camouflage. Finally, the paper outlines persisting challenges and suggests future development trends in the camouflage field via bioinspired structural color. [ABSTRACT FROM AUTHOR]

This paper presents the design of a new neuro-generator of pseudorandom number type PRNG Pseudorandom Number Generator, which produces complex sequences with an adequate bit distribution. The circuit is connected to a neuronal module with six impulse neurons with different behaviors: spike frequency adaptation, phasic spiking, mixed mode, phasic bursting, tonic bursting and tonic spiking. This module aims to generate a non-periodic signal that becomes the clock signal for one of the LFSRs Linear Feedback Shift Register that the neuro-generator has. To verify its correct operation, the neuro-generator was subjected to a series of tests where the frequencies of the impulse neurons were modified. This modification allows the generation of a greater number of pulses at the output of the neuronal module, to obtain sequences with different characteristics that pass different NIST statistical tests (National Institute of Standards and Technology of U.S.). The results show that the new neuro-generator maintains pseudo-randomness in the sequences obtained with different frequencies and it can be implemented on a reconfigurable FPGA Field Programmable Gate Array Virtex 7 xc7vx485t-2ffg1761 device. Therefore, it can be used for applications such as biological systems. [ABSTRACT FROM AUTHOR]

Developments in digital twins are driven by the availability of sensor technologies, big data, first principles knowledge, and advanced analytics. In this paper, we discuss these changes at a conceptual level, presenting a shift from nominal engineering, aiming at design optimisation, to performance engineering, aiming at adaptable monitoring diagnostic, prognostic, and prescriptive capabilities. A key element introduced here is the role of emulators in this transformation. Emulators, also called surrogate models or metamodels, provide monitoring and diagnostic capabilities. In particular, we focus on an optimisation goal combining optimised and robust performance derived from stochastic emulators. We demonstrate the methodology using two open-source examples and show how emulators can be used to complement finite element and computational fluid dynamic models in digital twin frameworks. The case studies consist of a mechanical system and a biological production process. [ABSTRACT FROM AUTHOR]

Periodically, the European Space Agency (ESA) updates scientific roadmaps in consultation with the scientific community. The ESA SciSpacE Science Community White Paper (SSCWP) 9, "Biology in Space and Analogue Environments", focusses in 5 main topic areas, aiming to address key community-identified knowledge gaps in Space Biology. Here we present one of the identified topic areas, which is also an unanswered question of life science research in Space: "How to Obtain an Integrated Picture of the Molecular Networks Involved in Adaptation to Microgravity in Different Biological Systems?" The manuscript reports the main gaps of knowledge which have been identified by the community in the above topic area as well as the approach the community indicates to address the gaps not yet bridged. Moreover, the relevance that these research activities might have for the space exploration programs and also for application in industrial and technological fields on Earth is briefly discussed. [ABSTRACT FROM AUTHOR]

Spiking neural networks (SNNs) are receiving increased attention because they mimic synaptic connections in biological systems and produce spike trains, which can be approximated by binary values for computational efficiency. Recently, the addition of convolutional layers to combine the feature extraction power of convolutional networks with the computational efficiency of SNNs has been introduced. This paper studies the feasibility of using a convolutional spiking neural network (CSNN) to detect anticipatory slow cortical potentials (SCPs) related to braking intention in human participants using an electroencephalogram (EEG). Data was collected during an experiment wherein participants operated a remote-controlled vehicle on a testbed designed to simulate an urban environment. Participants were alerted to an incoming braking event via an audio countdown to elicit anticipatory potentials that were measured using an EEG. The CSNN's performance was compared to a standard CNN, EEGNet and three graph neural networks via 10-fold cross-validation. The CSNN outperformed all the other neural networks, and had a predictive accuracy of 99.06% with a true positive rate of 98.50%, a true negative rate of 99.20% and an F1-score of 0.98. Performance of the CSNN was comparable to the CNN in an ablation study using a subset of EEG channels that localized SCPs. Classification performance of the CSNN degraded only slightly when the floating-point EEG data were converted into spike trains via delta modulation to mimic synaptic connections. [ABSTRACT FROM AUTHOR]

While combinatorial genetic data collection from biological systems in which quantitative phenotypes are controlled by active and inactive alleles of multiple genes (multi-gene systems) is becoming common, a standard analysis method for such data has not been established. The currently common approaches have three major drawbacks. First, although it is a long tradition in genetics, modeling the effect of an inactive allele (a null mutant allele) contrasted against that of the active allele (the wild-type allele) is not suitable for mechanistic understanding of multi-gene systems. Second, a commonly-used additive model (ANOVA with interaction) mathematically fails in estimation of interactions among more than two genes when the phenotypic response is not linear. Third, interpretation of higher-order interactions defined by an additive model is not intuitive. I derived an averaging model based on algebraic principles to solve all these problems within the framework of a general linear model. In the averaging model: the effect of the active allele is contrasted against the effect of the inactive allele for easier mechanistic interpretations; there is mathematical stability in estimation of higher-order interactions even when the phenotypic response is not linear; and interpretations of higher-order interactions are intuitive and consistent—interactions are defined as the mean effects of the last active genes added to the system. Thus, the key outcomes of this study are development of the averaging model, which is suitable for analysis of multi-gene systems, and a new, intuitive, and mathematically and interpretationally consistent definition of a genetic interaction, which is central to the averaging model. [ABSTRACT FROM AUTHOR]

Background: Joint analysis of multiple phenotypes in studies of biological systems such as Genome-Wide Association Studies is critical to revealing the functional interactions between various traits and genetic variants, but growth of data in dimensionality has become a very challenging problem in the widespread use of joint analysis. To handle the excessiveness of variables, we consider the sliced inverse regression (SIR) method. Specifically, we propose a novel SIR-based association test that is robust and powerful in testing the association between multiple predictors and multiple outcomes. Results: We conduct simulation studies in both low- and high-dimensional settings with various numbers of Single-Nucleotide Polymorphisms and consider the correlation structure of traits. Simulation results show that the proposed method outperforms the existing methods. We also successfully apply our method to the genetic association study of ADNI dataset. Both the simulation studies and real data analysis show that the SIR-based association test is valid and achieves a higher efficiency compared with its competitors. Conclusion: Several scenarios with low- and high-dimensional responses and genotypes are considered in this paper. Our SIR-based method controls the estimated type I error at the pre-specified level α . [ABSTRACT FROM AUTHOR]

The fate of organic matter in the environment, including anthropogenic chemicals, is largely predicated on the enzymatic capabilities of microorganisms. Microbes readily degrade, and thus recycle, most of the ~100,000 commercial chemicals used in modern society. Per‐ and polyfluorinated compounds (PFAS) are different. Many research papers posit that the general resistance of PFAS to microbial degradation is based in chemistry and that argument relates to the strength of the C–F bond. Here, I advance the opinion that the low biodegradability of PFAS is best formulated as a biological optimization problem, hence evolution. The framing of the problem is important. If it is framed around C–F bond strength, the major effort should focus on finding and engineering new C–F cleaving enzymes. The alternative, and preferred approach suggested here, is to focus on the directed evolution of biological systems containing known C–F cleaving systems. There are now reports of bacteria degrading and/or growing on multiply fluorinated arenes, alkenoic and alkanoic acids. The impediment to more efficient and widespread biodegradation in these systems is biological, not chemical. The rationale for this argument is made in the five sections below that follow the Introduction. [ABSTRACT FROM AUTHOR]

In biological systems, impulses are often used to model mutations in gene expression. In order to describe the instantaneity of the impulse, this paper employs a hybrid‐index model of impulsive Boolean networks with state‐triggered impulses. Based on this model, the time‐domain output controllability is investigated. Using the quotient mapping method, the hybrid‐domain solutions of this model are mapped to the time‐domain solutions, mathematically. Then a necessary and sufficient condition for output controllability in the time domain is presented. Finally, an example is given to demonstrate the correctness and effectiveness of the obtained results. [ABSTRACT FROM AUTHOR]

A critical function of polymeric matrices in biological systems is to exert selective control over the transport of thousands of nanoparticulate species. Utilizing "third-party" molecular anchors to crosslink nanoparticulates to the matrix is an effective strategy, and a trapped nanoparticulate formed a desired complex MAP that is necessary to keep the nanoparticulate immobilized at any given time. In this paper, the global solution and stability of a parabolic–ordinary-parabolic haptotaxis system to complex MAP are studied. First, the existence of a local classical solution to system (4) has been observed using fixed point argument and parabolic Schauder estimates. Furthermore, some a priori estimates that can raise the regularity estimate of the solution for the relatively complicated first equation of system (3) from L ρ to L 2 ρ ( ρ ≥ 1 ) are given; then, the local classic solution can thus extend to the global classic solution when the space dimension N ≤ 3 . Lastly, by using various analytical methods, a threshold value ξ 00 (ξ 00 < 0) is found, such that positive constant steady state (u ∗ , v ∗ , w ∗) becomes unstable when ξ < ξ 00 . Our results show that the haptotaxis plays a crucial role in determining the stability to the model (3), that is, it can have a destabilizing effect. [ABSTRACT FROM AUTHOR]

[Display omitted] • The JD-1 achieves sensitivity and selectivity detection of formaldehyde. • JD-1 has a ratiometric response to formaldehyde. • JD-1 can be used to detect formaldehyde in real food samples and soil. • JD-1 enables imaging of exogenous formaldehyde in living cells and zebrafish. • JD-1 can be used as a solid sensor to detect liquid and gaseous formaldehyde. Formaldehyde (FA) is a toxic, irritating, colorless gas molecule widely used in food preservation, construction, paint, pharmaceutical and chemical industries. However, FA may enter the air, water and soil during its release, causing ecological damage and jeopardizing human health. Therefore, it is significant to seek a simple and effective assay for detecting FA in environment, food, and biological systems. Here, we designed and synthesized a novel phenothiazine-based fluorescent probe, JD-1 , which exhibits ratiometric emission responses to FA at 400 nm and 505 nm. In addition, a solid sensor prepared by loading the probe JD-1 onto filter paper successfully achieved visual detection of liquid and gaseous FA. Notably, to further investigate the application scenarios of probe JD-1 in the detection of FA, we also prepared electrostatically spun fiber membranes by mixing probe JD-1 with PVDF-HFP, which were successfully used for the selective detection of FA. And the probe JD-1 possessed excellent stability in the detection of FA in real food samples, soil, live cells and zebrafish. Thus, this probe provides an attractive assay for the detection of FA in environment food, biological systems. [ABSTRACT FROM AUTHOR]