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Objectives: This study aimed to elucidate the connection between osteoclastic forkhead transcription factor O1 (FoxO1) and periodontitis and explore the underlying mechanism by which FoxO1 knockdown regulates osteoclast formation. Materials and Methods: A conventional ligature‐induced periodontitis model was constructed to reveal the alterations in the proportion of osteoclastic FoxO1 in periodontitis via immunofluorescence staining. Additionally, RNA sequencing (RNA‐seq) was performed to explore the underlying mechanisms of FoxO1 knockdown‐mediated osteoclastogenesis, followed by western blotting, quantitative polymerase chain reaction, and enzyme‐linked immunosorbent assay. Results: FoxO1+ osteoclasts were enriched in the alveolar bone in experimental periodontitis. Moreover, FoxO1 knockdown led to impaired osteoclastogenesis with low expression of osteoclast differentiation‐related genes, accompanied by an insufficient osteoclast maturation phenotype. Mechanistically, RNA‐seq revealed that the nuclear factor kappa B (NF‐κB) and nucleotide‐binding oligomerization domain‐like receptor family pyrin domain containing 3 (NLRP3) inflammasome signaling pathways were inhibited in FoxO1‐knockdown osteoclasts. Consistent with this, MCC950, an effective inhibitor of the NLRP3 inflammasome, substantially attenuated osteoclast formation. Conclusions: FoxO1 knockdown contributed to the inhibition of osteoclastogenesis by effectively suppressing NF‐κB signaling and NLRP3 inflammasome activation. This prospective study reveals the role of FoxO1 in mediating osteoclastogenesis and provides a viable therapeutic target for periodontitis treatment. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study was to identify and characterize the 100 most cited articles on Parkinson's disease (PD) and phenolic compounds (PCs). Methods: Articles were selected in the Web of Science Core Collection up to June 2022 based on predetermined inclusion criteria, and the following bibliometric parameters were extracted: the number of citations, title, keywords, authors, year, study design, tested PC and therapeutic target. MapChart was used to create worldwide networks, and VOSviewer software was used to create bibliometric networks. Descriptive statistical analysis was used to identify the most researched PCs and therapeutic targets in PD. Results: The most cited article was also the oldest. The most recent article was published in 2020. Asia and China were the continent and the country with the most articles in the list (55 and 29%, respectively). In vitro studies were the most common experimental designs among the 100 most cited articles (46%). The most evaluated PC was epigallocatechin. Oxidative stress was the most studied therapeutic target. Conclusion: Despite the demonstrations in laboratorial studies, the results obtained point to the need for clinical studies to better elucidate this association. [ABSTRACT FROM AUTHOR]

The Raman spectroscopy analysis technique has found extensive applications across various disciplines due to its exceptional convenience and efficiency, facilitating the analysis and identification of diverse substances. In recent years, owing to the escalating demand for high-efficiency analytical methods, deep learning models have progressively been introduced into the realm of Raman spectroscopy. However, the application of these models to portable Raman spectrometers has posed a series of challenges due to the computational intensity inherent to deep learning approaches. This paper proposes a lightweight classification model, named RepDwNet, for identifying 28 different types of biological blood. The model integrates advanced techniques such as multi-scale convolutional kernels, depth-wise separable convolutions, and residual connections. These innovations enable the model to capture features at different scales while preserving the coherence of feature data to the maximum extent. The experimental results demonstrate that the average recognition accuracy of the model on the reflective Raman blood dataset and the transmissive Raman blood dataset are 97.31% and 97.10%, respectively. Furthermore, by applying structural reparameterization to compress the well-trained model, it maintains high classification accuracy while significantly reducing the parameter size, thereby enhancing the speed of classification inference. This makes the model more suitable for deployment in portable and mobile devices. Additionally, the proposed model can be extended to various Raman spectroscopy classification scenarios. [ABSTRACT FROM AUTHOR]

Tardigrades, with over 1450 species, are important organisms in ecological understanding and are valuable biological models (e.g., due to their extremotolerant capabilities). While their biodiversity is better known in Europe and North America, Central and South America have only recently started making significant contributions. Through a comprehensive review of scientific literature, biological collections, web portal consultations, and the addition of new records, this study clarifies the current knowledge of tardigrade biodiversity in Colombia. Past research started in the early 20th century, but most data are unreliable due to information gaps and the absence of specimens in biological collections. The last decade has witnessed a resurgence in tardigrade research in Colombia, leading to new species descriptions and a more robust understanding of their biodiversity. Nevertheless, the majority of the territory remains unexplored. A total of 43 known species from our analysis are present in Colombia: 26 records accepted by the literature and 17 questioned in the literature but representing distinct taxa surely present in Colombia. Other species records are considered doubtful. Our study recommends considering mostly scientific records based on verifiable material deposited in scientific collections (highlighting their importance in studying and safeguarding biodiversity) and encourages future researchers to contribute while adhering to legal requirements. [ABSTRACT FROM AUTHOR]

In recent years, fractional calculus (FC) has filled in a hole in traditional calculus in terms of the effect of memory, which lets us know things about the past and present and guess what will happen in the future. It is very important to have this function, especially when studying biological models and integral equations. This paper introduces developed mathematical strategies for understanding a direct arrangement of fractional integro-differential equations (FIDEs). We have presented the least squares procedure and the Legendre strategy for discussing FIDEs. We have given the form of the Caputo concept fractional order operator and the properties. We have presented the properties of the shifted Legendre polynomials. We have shown the steps of the technique to display the solution. Some test examples are given to exhibit the precision and relevance of the introduced strategies. Mathematical outcomes show that this methodology is a comparison between the exact solution and the methods suggested. To show the theoretical results gained, the simulation of suggested strategies is given in eye-catching figures and tables. Program Mathematica 12 was used to get all of the results from the techniques that were shown. [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]

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]

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]

Due to factors such as climate change, natural disasters, and deforestation, most measurement processes and initial data may have errors. Therefore, models with imprecise parameters are more realistic. This paper constructed a new predator-prey model with an interval biological coefficient by using the interval number as the model parameter. First, the stability of the solution of the fractional order model without a diffusion term and the Hopf bifurcation of the fractional order α were analyzed theoretically. Then, taking the diffusion coefficient of prey as the key parameter, the Turing stability at the equilibrium point was discussed. The amplitude equation near the threshold of the Turing instability was given by using the weak nonlinear analysis method, and different mode selections were classified by using the amplitude equation. Finally, we numerically proved that the dispersal rate of the prey population suppressed the spatiotemporal chaos of the model. [ABSTRACT FROM AUTHOR]

Vibrational spectroscopy is a useful technique for probing chemical environments. The development of models that can reproduce the spectra of nitriles and azides is valuable because these probes are uniquely suited for investigating complex systems. Empirical vibrational spectroscopic maps are commonly employed to obtain the instantaneous vibrational frequencies during molecular dynamics simulations but often fail to adequately describe the behavior of these probes, especially in its transferability to a diverse range of environments. In this paper, we demonstrate several reasons for the difficulty in constructing a general-purpose vibrational map for methyl thiocyanate (MeSCN), a model for cyanylated biological probes. In particular, we found that electrostatics alone are not a sufficient metric to categorize the environments of different solvents, and the dominant features in intermolecular interactions in the energy landscape vary from solvent to solvent. Consequently, common vibrational mapping schemes do not cover all essential interaction terms adequately, especially in the treatment of van der Waals interactions. Quantum vibrational perturbation (QVP) theory, along with a combined quantum mechanical and molecular mechanical potential for solute–solvent interactions, is an alternative and efficient modeling technique, which is compared in this paper, to yield spectroscopic results in good agreement with experimental FTIR. QVP has been used to analyze the computational data, revealing the shortcomings of the vibrational maps for MeSCN in different solvents. The results indicate that insights from QVP analysis can be used to enhance the transferability of vibrational maps in future studies. [ABSTRACT FROM AUTHOR]

Many studies have shown that personalized exergames have a positive effect on promoting regular and proper exercise. However, there is no consensus on the design characteristics and evaluation of exergames. This systematic review of published research literature aimed to explore the general characteristics, personalization characteristics, and evaluation of personalized exergames for middle-aged and older people. We screened published studies in the Web of Science, Scopus, PubMed, ACM, and IEEE Xplore databases, extracted data, and performed a thematic analysis according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews checklist. Three main themes and nine subthemes were generated from 24 included papers. Moreover, a personalization model (FACTS) and evaluation system (PMSS) of exergames were developed. Personalized exergames had potential positive effects on motivating middle-aged and older people to exercise and improve their health, particularly physical, mental, and social health. However, more fine-grained studies on personalized exergames are necessary in the future. [ABSTRACT FROM AUTHOR]

This paper investigates the stabilization of the generalized Lotka–Volterra (GLV) biological model, which is affected by the asymptotically unbounded external disturbances, and presents some new results. Firstly, two stabilizers are proposed for the nominal GLV system. Then, some appropriate filters are designed and applied to asymptotically track the corresponding disturbances. Based on these filters, two disturbance-estimator (DE)-based controllers are presented to cancel the corresponding disturbances. Compared to the existing results, the advantage of this paper is in handling the asymptotically unbounded external disturbances effectively. Finally, the correctness and effectiveness of the proposed results are verified by computer simulation. [ABSTRACT FROM AUTHOR]

This paper presents an analytical solution to the model. By solving the system of nonlinear reaction-diffusion equations using Akbari–Ganji's method (AGM), we can derive the semi-analytical expressions for substrate and oxidized and reduced mediator concentrations. These models provide information about enzyme electrode mechanisms and their kinetics. These modeling results can be helpful for sensor design, optimization, and determining how the electrode will react. Further, we studied the effects of many physical and biological parameters on model prediction. The mathematical equations, which are nonlinear partial differential equations, are solved using AGM. Also, we derived the semi-analytical results for concentrations for saturated and unsaturated kinetics. It is verified that the proposed solution is validated by comparing it with numerical solutions. [ABSTRACT FROM AUTHOR]

We examine a biological population model of fractional order (FBPM) in this paper using the Riccati-Bernoulli sub-ODE approach. Many scenarios in computational biology make use of this fundamental fractional model. Of particular note is that our study's FBPM uses fractional derivatives to track changes in the density populations. The study is concerned with the construction of new solitary wave solutions for the FBPM, a system of two nonlinear fractional ordinary differential equations. In this investigation, we use the conformable derivative as the fractional derivative. The Backlund transformation is the foundation of the solution process. We create a variety of families of soliton wave solutions and explain different physical behaviours that are inherent in the problems we explore. In particular, we apply the suggested methods to investigate rational, periodic, and hyperbolic solutions. The solutions found in various classes provide insightful information about the underlying physical mechanisms. To sum up, our current methods are superior instruments for analyzing different families of solutions in fractional-order issues. [ABSTRACT FROM AUTHOR]

In this paper, we propose an efficient Newton linearized numerical method for the nonlinear time-fractional parabolic equations with distributed delay based on the Galerkin finite element method in space and the nonuniform L1 scheme in time. The term of distributed delay is approximated by using the compound trapezoidal formula. For the constructed numerical scheme, we mainly focus on the unconditional convergence and superconvergence without any time–space ratio restrictions, the key of which is the use of fractional discrete Grönwall inequality and time–space error splitting technique. Numerical tests for several biological models, including the fractional single-species population model with distributed delay, the fractional diffusive Nicholson's blowflies equation with distributed delay, and the fractional diffusive Mackey-Glass equation with distributed delay, are conducted to confirm the theoretical results. Finally, combined with the nonunifom Alikhanov scheme in time and the FEM in space, we extend a higher-order Newton linearized numerical scheme for the nonlinear time-fractional parabolic equations with distributed delay and give some numerical tests for some biological models. [ABSTRACT FROM AUTHOR]

Currently, many methods that could estimate the effects of conditions on a given biological target require either strong modelling assumptions or separate screens. Traditionally, many conditions and targets, without doing all possible experiments, could be achieved by driven experimentation or several mathematical methods, especially conversational machine learning methods. However, these methods still could not avoid and replace manual labels completely. This paper presented a meta-active machine learning method to resolve this problem. This project has used nine traditional machine learning methods to compare their accuracy and running time. In addition, this paper analyzes the meta-active machine learning method (MAML) compared with a classical screening method and progressive experiments. The obtained results show that applying this method yields the best experimental results on the current dataset. [ABSTRACT FROM AUTHOR]

Controller motifs are simple biomolecular reaction networks with negative feedback. They can explain how regulatory function is achieved and are often used as building blocks in mathematical models of biological systems. In this paper we perform an extensive investigation into structural identifiability of controller motifs, specifically the so–called basic and antithetic controller motifs. Structural identifiability analysis is a useful tool in the creation and evaluation of mathematical models: it can be used to ensure that model parameters can be determined uniquely and to examine which measurements are necessary for this purpose. This is especially useful for biological models where parameter estimation can be difficult due to limited availability of measureable outputs. Our aim with this work is to investigate how structural identifiability is affected by controller motif complexity and choice of measurements. To increase the number of potential outputs we propose two methods for including flow measurements and show how this affects structural identifiability in combination with, or in the absence of, concentration measurements. In our investigation, we analyze 128 different controller motif structures using a combination of flow and/or concentration measurements, giving a total of 3648 instances. Among all instances, 34% of the measurement combinations provided structural identifiability. Our main findings for the controller motifs include: i) a single measurement is insufficient for structural identifiability, ii) measurements related to different chemical species are necessary for structural identifiability. Applying these findings result in a reduced subset of 1568 instances, where 80% are structurally identifiable, and more complex/interconnected motifs appear easier to structurally identify. The model structures we have investigated are commonly used in models of biological systems, and our results demonstrate how different model structures and measurement combinations affect structural identifiability of controller motifs. Author summary: Creating a mathematical model of a biological system can be a powerful way to gain insight into the behavior of the system. However, the accuracy and quality of model predictions depend heavily on model parameters. Compared to traditional human–engineered systems, such as mechanical or electrical systems, the availability of measurable outputs is severely limited for many biological systems. Furthermore, parameter estimation for biological models is often both challenging and associated with high cost. In this context, structural identifiability analysis is a helpful tool for finding the smallest or easiest to perform set of measurements that is sufficient to uniquely determine the model parameters. In this paper, we investigate a group of biological models called controller motifs, and we examine how varying model complexity and choice of measurements affect structural identifiability. We propose two alternative ways to include flow measurements as model output and show that structural identifiability can be achieved using a combination of concentration and/or flow measurements. Controller motifs can be used in a wide range of biological models, and our results can contribute to create structurally identifiable models. [ABSTRACT FROM AUTHOR]

Discrete fractional models with reaction-diffusion have gained significance in the scientific field in recent years, not only due to the need for numerical simulation but also due to the stated biological processes. In this paper, we investigate the problem of synchronization-control in a fractional discrete nonlinear bacterial culture reaction-diffusion model using the Caputo h-difference operator and a second-order central difference scheme and an L1 finite difference scheme after deriving the discrete fractional version of the well-known Degn–Harrison system and Lengyel–Epstein system. Using appropriate techniques and the direct Lyapunov method, the conditions for full synchronization are determined.Furthermore, this research shows that the L1 finite difference scheme and the second-order central difference scheme may successfully retain the properties of the related continuous system. The conclusions are proven throughout the paper using two major biological models, and numerical simulations are carried out to demonstrate the practical use of the recommended technique. [ABSTRACT FROM AUTHOR]

Combining different features inspired by biological systems is necessary to obtain uncommon and unique multifunctional biologically inspired conceptual designs. The Expandable Domain Integrated Design (xDID) model is proposed to facilitate the multifunctional concept generation process. The xDID model extends the previously defined Domain Integrated Design (DID) method. The xDID model classifies biological features by their feature characteristics taken from various case-based bio-inspired design examples into their respective geometric designations called domains. The classified biological features are mapped to the respective plant and animal tissues from which they originate. Furthermore, the paper proposes a representation of the functions exhibited by the biological features at the embodiment level as a combination of the integrated structure (multiscale) and the structural strategy associated with the integrated structure. The xDID model is validated using three multifunctional bio-inspired design case studies at the end of the paper. [ABSTRACT FROM AUTHOR]

Simple Summary: The word "model" can be used with different meanings and different contexts, like a model student, clay models or a model railway. In some cases, the context can clarify exactly what is meant by "model", but sometimes several meanings of model can be present in one area. For instance, with reference to cancer research, there can be ambiguity for what is meant by model. This paper reviews the use of the word model as related to cancer research and within the specific area of the microenvironment that surrounds a cancer tumour. The review grouped different definitions of model into four categories (model organisms, in vitro models, mathematical models and computational models) and explored what is meant in each case, mentioning the advantages and disadvantages of the different models Next, a quantitative investigation of the scientific publications listed in the database of the United States National Library of Medicine was performed by counting the frequencies of use of these terms, as well as the components of the microenvironments and the organs modelled with these techniques. The Oxford English Dictionary includes 17 definitions for the word "model" as a noun and another 11 as a verb. Therefore, context is necessary to understand the meaning of the word model. For instance, "model railways" refer to replicas of railways and trains at a smaller scale and a "model student" refers to an exemplary individual. In some cases, a specific context, like cancer research, may not be sufficient to provide one specific meaning for model. Even if the context is narrowed, specifically, to research related to the tumour microenvironment, "model" can be understood in a wide variety of ways, from an animal model to a mathematical expression. This paper presents a review of different "models" of the tumour microenvironment, as grouped by different definitions of the word into four categories: model organisms, in vitro models, mathematical models and computational models. Then, the frequencies of different meanings of the word "model" related to the tumour microenvironment are measured from numbers of entries in the MEDLINE database of the United States National Library of Medicine at the National Institutes of Health. The frequencies of the main components of the microenvironment and the organ-related cancers modelled are also assessed quantitatively with specific keywords. Whilst animal models, particularly xenografts and mouse models, are the most commonly used "models", the number of these entries has been slowly decreasing. Mathematical models, as well as prognostic and risk models, follow in frequency, and these have been growing in use. [ABSTRACT FROM AUTHOR]

SARS-CoV-2 has posed a threat to the health of people around the world because of its strong transmission and high virulence. Currently, there is no specific medicine for the treatment of COVID-19. However, for a wide variety of medicines used to treat COVID-19, traditional Chinese medicine (TCM) plays a major role. In this paper, the effective treatment of COVID-19 using TCM was consulted first, and several Chinese medicines that were frequently used apart from their huge role in treating it were found. Then, when exploring the active ingredients of these herbs, it was discovered that most of them contained flavonoids. Therefore, the structure and function of the potential active substances of flavonoids, including flavonols, flavonoids, and flavanes, respectively, are discussed in this paper. According to the screening data, these flavonoids can bind to the key proteins of SARS-CoV-2, 3CLpro, PLpro, and RdRp, respectively, or block the interface between the viral spike protein and ACE2 receptor, which could inhibit the proliferation of coronavirus and prevent the virus from entering human cells. Besides, the effects of flavonoids on the human body systems are expounded on in this paper, including the respiratory system, digestive system, and immune system, respectively. Normally, flavonoids boost the body's immune system. However, they can suppress the immune system when over immunized. Ultimately, this study hopes to provide a reference for the clinical drug treatment of COVID-19 patients, and more TCM can be put into the market accordingly, which is expected to promote the development of TCM on the international stage. [ABSTRACT FROM AUTHOR]

Objective. In this paper, we present MONAS (MicrOdosimetry-based modelliNg for relative biological effectiveness (RBE) ASsessment) toolkit. MONAS is a TOPAS Monte Carlo extension, that combines simulations of microdosimetric distributions with radiobiological microdosimetry-based models for predicting cell survival curves and dose-dependent RBE. Approach. MONAS expands TOPAS microdosimetric extension, by including novel specific energy scorers to calculate the single- and multi-event specific energy microdosimetric distributions at different micrometer scales. These spectra are used as physical input to three different formulations of the microdosimetric kinetic m odel, and to the generalized stochastic microdosimetric model (GSM2), to predict dose-dependent cell survival fraction and RBE. MONAS predictions are then validated against experimental microdosimetric spectra and in vitro survival fraction data. To show the MONAS features, we present two different applications of the code: (i) the depth-RBE curve calculation from a passively scattered proton SOBP and monoenergetic 12C-ion beam by using experimentally validated spectra as physical input, and (ii) the calculation of the 3D RBE distribution on a real head and neck patient geometry treated with protons. Main results. MONAS can estimate dose-dependent RBE and cell survival curves from experimentally validated microdosimetric spectra with four clinically relevant radiobiological models. From the radiobiological characterization of a proton SOBP and 12C fields, we observe the well-known trend of increasing RBE values at the distal edge of the radiation field. The 3D RBE map calculated confirmed the trend observed in the analysis of the SOBP, with the highest RBE values found in the distal edge of the target. Significance. MONAS extension offers a comprehensive microdosimetry-based framework for assessing the biological effects of particle radiation in both research and clinical environments, pushing closer the experimental physics-based description to the biological damage assessment, contributing to bridging the gap between a microdosimetric description of the radiation field and its application in proton therapy treatment with variable RBE. [ABSTRACT FROM AUTHOR]

Successful bioinspired design depends on practitioners' access to biological data in a relevant form. Although multiple open-access biodiversity databases exist, their presentation is often adapted to life scientists, rather than bioinspired designers. In this paper, we present a new tool, "Bioinspire-Explore", for navigating biodiversity data in order to uncover biological systems of interest for a range of sectors. Bioinspire-Explore allows users to search for inspiring biological models via taxa (species, genera, etc.) as an entry point. It provides information on a taxon's position in the "tree of life", its distribution and climatic niche, as well as its appearance. Bioinspire-Explore also shows users connections in the bioinspiration literature between their taxon of interest and associated biological processes, habitats, and physical measurements by way of their semantic proximity. We believe Bioinspire-Explore has the potential to become an indispensable resource for both biologists and bioinspired designers in different fields. [ABSTRACT FROM AUTHOR]

For patients who had testicular tissue cryopreserved before receiving gonadotoxic therapies, transplantation of testicular tissues and cells has been recommended as a potential therapeutic option. There are no studies that indicate the generation of sperm after human immature testicular tissue (ITT) or spermatogonial stem cells (SSCs) transplantation. The use of releasing scaffolds and localized drug delivery systems as well as the optimizing transplantation site can play an effective role in increasing the efficiency and improving the quality of testicular tissue and cell transplantation in animal models. Current research is focused on optimizing ITT and cell transplantation, the use of releasing scaffolds, and the selection of the right transplantation site that might restore sperm production or male infertility treatment. By searching the PubMed and Google Scholar databases, original and review papers were collected. Search terms were relevant for SSCs and tissue transplantation. In this review, we'll focus on the potential advantages of using scaffolds and choosing the right transplantation site to improve transplantation outcomes. [ABSTRACT FROM AUTHOR]

Analysis of the method of using sonography during surgery in animals with polycystic kidney disease is an urgent task since firstly, sonography is a safe and non-invasive method of examination, which allows determining the structural features of the kidneys before, during, and after surgery. Secondly, from the standpoint of improving the results of surgery, sonography helps to clarify the localisation of cysts and determine their size. Thirdly, an important factor in the use of sonography is the reduction of pain and the risk of postoperative complications. In addition, due to this method of kidney examination, it is possible to more accurately determine the optimal route of access to cysts, which helps to reduce tissue injury and ensures rapid recovery of the animal after surgery. The purpose of the study is to analyse in detail and describe the method of using sonography during surgery in animals with polycystic kidney disease. The study focuses on the need to determine how sonography affects reducing the duration of surgery, improving the quality of cyst removal, and reducing the risk of complications during abdominal surgery. The approach in this study is based on the analysis of scientific papers on this subject, in particular on the experience of veterinarians who have already used sonography during abdominal operations in animals with polycystic kidney disease. Thus, special techniques of sonography and surgical treatment of kidney cysts include dopplerography, colour dopplerography, 3D and 4D sonography, elastography, introperative sonography, intraperitoneal sonography, and duplex scanning of renal arteries and veins. Surgical methods of treatment include extraction of individual cysts, drainage of cysts, resection, and nephrectomy. The use of sonography at different stages of surgical intervention helps to optimise the operation process, reduce the risk of complications, and contribute to the introduction of new approaches in the treatment of animals with polycystic kidney disease, which will substantially improve their quality of life [ABSTRACT FROM AUTHOR]

The gut microbiota (GM), as a forgotten organ, refers to the microbial community that resides in the gastrointestinal tract and plays a critical role in a variety of physiological activities in different body organs. The GM affects its targets through neurological, metabolic, immune, and endocrine pathways. The GM is a dynamic system for which exogenous and endogenous factors have negative or positive effects on its density and composition. Since the mid-twentieth century, laboratory animals are known as the major tools for preclinical research; however, each model has its own limitations. So far, two main models have been used to explore the effects of the GM under normal and abnormal conditions: the isolated germ-free and antibiotic-treated models. Both methods have strengths and weaknesses. In many fields of host-microbe interactions, research on these animal models are known as appropriate experimental subjects that enable investigators to directly assess the role of the microbiota on all features of physiology. These animal models present biological model systems to either study outcomes of the absence of microbes, or to verify the effects of colonization with specific and known microbial species. This paper reviews these current approaches and gives advantages and disadvantages of both models. [ABSTRACT FROM AUTHOR]

The authors of the paper "Bayesian Graphical Models for Modern Biological Applications" have put forward an important framework for making graphical models more useful in applied settings. In this discussion paper, we give a number of suggestions for making this framework even more suitable for practical scenarios. Firstly, we show that an alternative and simplified definition of covariate might make the framework more manageable in high-dimensional settings. Secondly, we point out that the inclusion of missing variables is important for practical data analysis. Finally, we comment on the effect that the Gaussianity assumption has in identifying the underlying conditional independence graph and how this can be circumvented. The Bayesian framework proposed by the authors is flexible enough to accommodate extensions that can deal with these aspects, which are often encountered in real data analyses such as the complex modern applications considered by the authors. [ABSTRACT FROM AUTHOR]

Mechanistic models have been used for centuries to describe complex interconnected processes, including biological ones. As the scope of these models has widened, so have their computational demands. This complexity can limit their suitability when running many simulations or when real-time results are required. Surrogate machine learning (ML) models can be used to approximate the behaviour of complex mechanistic models, and once built, their computational demands are several orders of magnitude lower. This paper provides an overview of the relevant literature, both from an applicability and a theoretical perspective. For the latter, the paper focuses on the design and training of the underlying ML models. Application-wise, we show how ML surrogates have been used to approximate different mechanistic models. We present a perspective on how these approaches can be applied to models representing biological processes with potential industrial applications (e.g., metabolism and whole-cell modelling) and show why surrogate ML models may hold the key to making the simulation of complex biological systems possible using a typical desktop computer. [ABSTRACT FROM AUTHOR]

We develop and investigate a model for sludge production in the activated sludge process when a biological reactor is coupled to a sludge disintegration unit (SDU). The model for the biological reactor is a slimmed down version of the activated sludge model 1 in which only processes related to carbon are retained. Consequently, the death-regeneration concept is included in our model which is an improvement on almost all previous models. This provides an improved representation of the total suspended solids in the biological reactor, which is the key parameter of interest. We investigate the steady-state behaviour of this system as a function of the residence time within the biological reactor and as a function of parameters associated with the operation of the SDU. A key parameter is the sludge disintegration factor. As this parameter is increased the concentration of total suspended solids within the biological reactor decreases at the expense increasing the chemical oxygen demand in the effluent stream. The existence of a maximum acceptable chemical oxygen demand in the effluent stream therefore imposes a maximum achievable reduction in the total suspended solids. This paper improves our theoretical understanding of the utility of sludge disintegration as a means to reduce excess sludge formation. As an aside to the main thrust of our paper we investigate the common assumption that the sludge disintegration processes occur on a much shorter timescale than the biological processes. We show that the disintegration processes must be exceptional slow before the inclusion of the biological processes becomes important. [ABSTRACT FROM AUTHOR]

Despite the advances in burn wound management, infection is a primary cause of morbidity, with infected burn wounds being responsible for 51% of burn related deaths. Because of the growing threat of multidrug resistant pathogens in burn injuries, novel therapeutic innovation remain important. This paper presents a comparison of common models and methods that support the development of novel burn wound antimicrobial treatments, of commonly available burn wound models 74% are performed in vivo, 23% are in vitro and just 3% use ex vivo tissue. The manufacturing of burn wound care products requires careful attention to materials, sterilisation, quality control, regulatory compliance and product design. By addressing these considerations systematically, manufacturers can successfully bring innovation solutions to the burn wound market while ensuring patient safety and product quality. [ABSTRACT FROM AUTHOR]

The study of neural cell behavior is one of the exciting fields that has attracted scientists' attention since 1890. Accordingly, many efforts have been made to model the neural cell specifying different neuron behaviors and characteristics. These studies mainly focus on two primary targets: finding new computing paradigms and providing better models and tools for medical applications. A critical model for studying biological neurons' behavior is the Hodgkin–Huxley neuron model (HH for short), presented in 1952. In this paper, the model is realized and verified using only one multiplier with some simplification. By comparing the original and simplified HH models, the RMSE and NRMSE are 1.83 and 0.016 for neuron membrane output potential, respectively, at the injected current of 20 uA. The dynamical behaviors of the model are also studied to ensure stability. The proposed model is synthesized and implemented on an FPGA platform, where the outputs are examined, and the results are presented as proof of concept. [ABSTRACT FROM AUTHOR]

In this paper, a new stochastic four-species predator-prey model with disease in the first prey is proposed and studied. First, we present the stochastic model with some biological assumptions and establish the existence of globally positive solutions. Moreover, a condition for species to be permanent and extinction is provided. The above properties can help to save the dangered population in the ecosystem. Through Lyapunov functions, we discuss the asymptotic stability of a positive equilibrium solution for our model. Furthermore, it is also shown that the system has a stationary distribution and indicating the existence of a stable biotic community. Finally, our results of the proposed model have revealed the effect of random fluctuations on the four species ecosystem when adding the alternative food sources for the predator population. To illustrate our theoretical findings, some numerical simulations are presented. [ABSTRACT FROM AUTHOR]

Simple Summary: The expression of the cysteine–glutamate cotransporter, system xc−, is increased in cancer cells across many cancer types. Imaging system xc− provides new insights into tumour behaviour. The radiotracer (4S)-4-(3-[18F]Fluoropropyl)-L-glutamic acid (18F-FSPG) is specifically transported by system xc−, allowing for a non-invasive method of measuring this transporter's activity. This review summarises the data available on the use of 18F-FSPG in human cancer patients, exploring its advantages and disadvantages, and suggests possible future uses of 18F-FSPG in the assessment of early treatment response and treatment resistance. System xc− is upregulated in cancer cells and can be imaged using novel radiotracers, most commonly with (4S)-4-(3-[18F]fluoropropyl)-L-glutamic acid (18F-FSPG). The aim of this review was to summarise the use of 18F-FSPG in humans, explore the benefits and limitations of 18F-FSPG, and assess the potential for further use of 18F-FSPG in cancer patients. To date, ten papers have described the use of 18F-FSPG in human cancers. These studies involved small numbers of patients (range 1–26) and assessed the use of 18F-FSPG as a general oncological diagnostic agent across different cancer types. These clinical trials were contrasting in their findings, limiting the scope of 18F-FSPG PET/CT as a purely diagnostic agent, primarily due to heterogeneity of 18F-FSPG retention both between cancer types and patients. Despite these limitations, a potential further application for 18F-FSPG is in the assessment of early treatment response and prediction of treatment resistance. Animal models of cancer have shown that changes in 18F-FSPG retention following effective therapy precede glycolytic changes, as indicated by 18F-FDG, and changes in tumour volume, as measured by CT. If these results could be replicated in human clinical trials, imaging with 18F-FSPG PET/CT would offer an exciting route towards addressing the currently unmet clinical needs of treatment resistance prediction and early imaging assessment of therapy response. [ABSTRACT FROM AUTHOR]

Simple Summary: As a first-line chemotherapeutic for colorectal cancer (CRC), FOLFOX, XELOX, and similar regimens are recommended as adjuvant therapies following CRC surgery, according to the NCCN guidelines. In stage IV patients, targeted therapies like cetuximab and bevacizumab are used in combination. The decision to add either irinotecan-based regimens or oxaliplatin-based regimens, FOLFIRI is typically based on a physician's subjective judgment due to the lack of clear selection criteria. Therefore, having the ability to precisely predict the therapeutic response to these two drugs could help prioritize the use of the most effective drug in stage IV patients. Previous research efforts aimed to predict therapeutic responses to these two drugs using organoids. While irinotecan showed promising predictability, predicting the response to oxaliplatin remained challenging. In this study, new drug screening conditions were identified to address this issue. Through additional research using CRC organoids, this paper contributes to the prediction of drug responses in CRC patients. Colorectal cancer is the third most common cancer in the world, with an annual incidence of 2 million cases. The success of first-line chemotherapy plays a crucial role in determining the disease outcome. Therefore, there is an increasing demand for precision medicine to predict drug responses and optimize chemotherapy in order to increase patient survival and reduce the related side effects. Patient-derived organoids have become a popular in vitro screening model for drug-response prediction for precision medicine. However, there is no established correlation between oxaliplatin and drug-response prediction. Here, we suggest that organoid culture conditions can increase resistance to oxaliplatin during drug screening, and we developed a modified medium condition to address this issue. Notably, while previous studies have shown that survivin is a mechanism for drug resistance, our study observed consistent survivin expression irrespective of the culture conditions and oxaliplatin treatment. However, clusterin induced apoptosis inhibition and cell survival, demonstrating a significant correlation with drug resistance. This study's findings are expected to contribute to increasing the accuracy of drug-response prediction in patient-derived APC mutant colorectal cancer organoids, thereby providing reliable precision medicine and improving patient survival rates. [ABSTRACT FROM AUTHOR]

Simple Summary: Pancreatic tumours present significant treatment challenges due to their resistance to chemotherapy and complex tumour microenvironment. Choosing the appropriate preclinical models and understanding how their characteristics affect drug delivery to the tumours is essential for designing clinically relevant experiments. This study investigates pancreatic tumours growing orthotopically or subcutaneously and presents the properties of their tumour microenvironments and their impacts on drug delivery. Pancreatic ductal adenocarcinoma (PDAC) remains a challenging malignancy, mainly due to its resistance to chemotherapy and its complex tumour microenvironment characterised by stromal desmoplasia. There is a need for new strategies to improve the delivery of drugs and therapeutic response. Relevant preclinical tumour models are needed to test potential treatments. This paper compared orthotopic and subcutaneous PDAC tumour models and their suitability for drug delivery studies. A novel aspect was the broad range of tumour properties that were studied, including tumour growth, histopathology, functional vasculature, perfusion, immune cell infiltration, biomechanical characteristics, and especially the extensive analysis of the structure and the orientation of the collagen fibres in the two tumour models. The study unveiled new insights into how these factors impact the uptake of a fluorescent model drug, the macromolecule called 800CW. While the orthotopic model offered a more clinically relevant microenvironment, the subcutaneous model offered advantages for drug delivery studies, primarily due to its reproducibility, and it was characterised by a more efficient drug uptake facilitated by its collagen organisation and well-perfused vasculature. The tumour uptake seemed to be influenced mainly by the structural organisation and the alignment of the collagen fibres and perfusion. Recognising the diverse characteristics of these models and their multifaceted impacts on drug delivery is crucial for designing clinically relevant experiments and improving our understanding of pancreatic cancer biology. [ABSTRACT FROM AUTHOR]

Beer ranks third in terms of consumption worldwide, and its technology is constantly being improved and perfected. The boom in craft brewing has led to the production of thousands of new types of beer, but most often without scientific justification of the technological decisions made. This paper discusses the approaches to the modeling of the composition of the malt mixture and the mode of lager beer production. A special cubic simplex-lattice design with two replications was used to model the mixture with three malt types: Pilsner, Caramel Pils and Caramel Munich type 2. Models for the main brewing characteristics, i.e., wort extract and color, as well as models for the biological parameters of the mash, i.e., phenolic compound content and antioxidant potential, were developed using different methods. Multi-objective optimization was carried out and a specific mixture was developed for the production of lager beer. The influence of acidulated malt, lactic acid and CaCl2 additions on the extract yield and malt mash filtration time were established through one-factor experiments. The extract and fermentable sugar yield during individual pauses in the mashing mode was studied with a view to its optimization. [ABSTRACT FROM AUTHOR]

Aims: Spatio‐temporal processes play a key role in ecology, from genes to large‐scale macroecological and biogeographical processes. Existing methods studying such spatio‐temporally structured data either simplify the dynamic structure or the complex interactions of ecological drivers. The aim of this paper is to present a generic method for ecological research that allows analysing spatio‐temporal patterns of biological processes at large spatial scales by including the time‐varying variables that drive these dynamics. Location: Global analysis at the level of 272 regions. Methods: We introduce a method called relational event modelling (REM). REM relies on temporal interaction dynamics that encode sequences of relational events connecting a sender node to a recipient node at a specific point in time. We apply REM to the spread of alien species around the globe between 1880 and 2005, following accidental or deliberate introductions into geographical regions outside of their native range. In this context, a relational event represents the new occurrence of an alien species given its former distribution. Results: The application of relational event models to the first reported invasions of 4835 established alien species outside of their native ranges from four major taxonomic groups enables us to unravel the main drivers of the dynamics of the spread of invasive alien species. Combining the alien species first records data with other spatio‐temporal information enables us to discover which factors have been responsible for the spread of species across the globe. Besides the usual drivers of species invasions, such as trade, land use and climatic conditions, we also find evidence for species‐interconnectedness in alien species spread. Conclusions: Relational event models offer the capacity to account for the temporal sequences of ecological events such as biological invasions and to investigate how relationships between these events and potential drivers change over time. [ABSTRACT FROM AUTHOR]

In ecosystems, almost all organisms suffer from external influences, which are assumed to be random perturbations. The aim of this paper is to investigate the effects of random perturbations on interacting populations. To solve this problem, we propose a discrete-time plant quality and larch budmoth interaction model with biological control. First of all, the model is theoretically analyzed, and the dynamic behavior of the population is qualitatively analyzed by numerical simulation. In addition, random perturbations are added to the model to observe the effects of random perturbations on the dynamic behavior of the budmoth population. The main results show that a random perturbation changes the number of blurred orbits of the system compared to the single population model with the same perturbation. Undeniably, it enhances the stability of the system. The model results, after adding random perturbations, are closer to reality, and in practice, we can also use random perturbations for resource management and pest control. [ABSTRACT FROM AUTHOR]

Due to various imprecisions in nature, imprecise parameters in biological modeling should be taken into account. This paper studies the spatial dynamics of an imprecise prey-predator model of the Leslie–Gower type by presenting imprecise parameters as interval parameters. First, conditions of Turing instability are obtained via bifurcation analysis and interval-valued functions. Then, the effects of interval parameters on pattern selection are discussed via multiple-scale analysis. We discover that when all the parameters of the model are interval parameters, the value of the controlled parameter increases, and the range of the pattern selection domain expands as the value of the interval variable increases, i.e., both the controlled parameter and boundary of the pattern selection domain are interval numbers. Finally, under the effects of the interval parameters of diffusion and the prey's conversion rate into biomass for the predator, the density of the prey decreases or increases, respectively, and the structure or the microstructure of the pattern of the model changes with the growing value of the interval variable. This paper provides a new perspective on the study of the spatial predator-prey model. [ABSTRACT FROM AUTHOR]

This paper investigates the soliton wave solution of the nonlinear biological population (NBP) model by employing a novel computational scheme. The selected model for this study describes the logistics of the population because of births and deaths. Some novel structures of the NBP model's solutions, are obtained such as exponential, trigonometric, and hyperbolic. These solutions are clarified through some distinct graphs in contour three plot, three-dimensional, and two-dimensional plots. The Hamiltonian system's characterizations are used to check the obtained solutions' stability. The solutions' accuracy is checked by handling the NBP model through the variational iteration (VI) method. The matching between analytical and semi-analytical solutions shows the accuracy of the obtained solutions. The method's performance shows its effectiveness, power, and ability to apply to many nonlinear evolution equations. [ABSTRACT FROM AUTHOR]

The International Union of Basic and Clinical Pharmacology (IUPHAR) Geriatric Committee aims to improve the use of drugs in older adults and develop new therapeutic approaches for the syndromes and diseases of old age through advocacy, education, and research. In the present paper, we propose strategies relevant to drug development and evaluation, spanning preclinical and the full range of clinical studies. Drugs for older adults need to consider not only age, but also other characteristics common in geriatric patients, such as multimorbidity, polypharmacy, falls, cognitive impairment, and frailty. The IUPHAR Geriatric Committee's position statement on 'Measurement of Frailty in Drug Development and Evaluation' is included, highlighting 12 key principles that cover the spectrum of translational research. We propose that where older adults are likely to be major users of a drug, that frailty is measured at baseline and as an outcome. Preclinical models that replicate the age, frailty, duration of exposure, comorbidities, and co‐medications of the proposed patients may improve translation. We highlight the potential application of recent technologies, such as physiologically based pharmacokinetic–pharmacodynamic modeling informed by frailty biology, and Artificial Intelligence, to inform personalized medicine for older patients. Considerations for the rapidly aging populations in low‐ and middle‐income countries related to health‐care and clinical trials are outlined. Involving older adults, their caregivers and health‐care providers in all phases of research should improve drug development, evaluation, and outcomes for older adults internationally. [ABSTRACT FROM AUTHOR]

Epigenetics in the form of DNA methylation and other processes is an established property of genotypes and a focus of empirical research. Yet, there remain fundamental gaps in the evolutionary theory of epigenetics. To support a comprehensive understanding of epigenetics, this paper investigates theoretically the combined effects of deleterious mutation and epimutation with and without inbreeding. Both spontaneous epimutation and paramutation are considered to cover a broader range of epigenetic phenomena. We find that inbreeding generally reduces the amount of segregating deleterious genetic and epigenetic variation at equilibrium, although interestingly inbreeding can also increase the amount of deleterious genetic or epigenetic variation. Furthermore, we also demonstrate that epimutation indirectly can cause increased or decreased deleterious genetic variation at equilibrium relative to classic expectations, which is particularly evident when paramutation is occurring. With the addition of deleterious epimutation, there may be significantly increased purging of deleterious variation in more inbred populations and a significantly increased amount of segregating deleterious variation in more outbred populations, with notable exceptions. [ABSTRACT FROM AUTHOR]

Modern small‐angle scattering (SAS) experiments with neutrons (SANS) or X‐rays (SAXS) combined with contrast variation provide comprehensive information about the structure of large multicomponent macromolecules in solution and allow the size, shape and relative arrangement of each component to be mapped out. To obtain such information, it is essential to perform well designed experiments, in which all necessary steps, from assessing sample suitability to structure modeling, are properly executed. This paper describes α‐SAS, an integrative approach that is useful for effectively planning a biological contrast‐variation SAS experiment. The accurate generation of expected experimental intensities using α‐SAS allows the substantial acceleratation of research into the structure and function of biomacromolecules by minimizing the time and costs associated with performing a SAS experiment. The method is validated using a few basic structures with known analytical expressions for scattering intensity and using experimental SAXS data from Arabidopsis β‐amylase 1 protein and SANS data from the histidine kinase–Sda complex and from human dystrophin without and with a membrane‐mimicking nanodisk. Simulation of a SANS contrast‐variation experiment is performed for synthetic nanobodies that effectively neutralize SARS‐CoV‐2. [ABSTRACT FROM AUTHOR]

Background: FLASH proton therapy has the potential to reduce side effects of conventional proton therapy by delivering a high dose of radiation in a very short period of time. However, significant progress is needed in the development of FLASH proton therapy. Increasing the dose rate while maintaining dose conformality may involve the use of advanced beam‐shaping technologies and specialized equipment such as 3D patient‐specific range modulators, to take advantage of the higher transmission efficiency at the highest energy available. The dose rate is an important factor in FLASH proton therapy, but its definition can vary because of the uneven distribution of the dose over time in pencil‐beam scanning (PBS). Purpose: Highlight the distinctions, both in terms of concept and numerical values, of the various definitions that can be established for the dose rate in PBS proton therapy. Methods: In an in silico study, five definitions of the dose rate, namely the PBS dose rate, the percentile dose rate, the maximum percentile dose rate, the average dose rate, and the dose averaged dose rate (DADR) were analyzed first through theoretical comparison, and then applied to a head and neck case. To carry out this study, a treatment plan utilizing a single energy level and requiring the use of a patient‐specific range modulator was employed. The dose rate values were compared both locally and by means of dose rate volume histograms (DRVHs). Results: The PBS dose rate, the percentile dose rate, and the maximum percentile dose are definitions that are specifically designed to take into account the time structure of the delivery of a PBS treatment plan. Although they may appear similar, our study shows that they can vary locally by up to 10%. On the other hand, the DADR values were approximately twice as high as those of the PBS, percentile, and maximum percentile dose rates, since the DADR disregards the periods when a voxel does not receive any dose. Finally, the average dose rate can be defined in various ways, as discussed in this paper. The average dose rate is found to be lower by a factor of approximately 1/2 than the PBS, percentile, and maximum percentile dose rates. Conclusions: We have shown that using different definitions for the dose rate in FLASH proton therapy can lead to variations in calculated values ranging from a few percent to a factor of two. Since the dose rate is a critical parameter in FLASH radiation therapy, it is essential to carefully consider the choice of definition. However, to make an informed decision, additional biological data and models are needed. [ABSTRACT FROM AUTHOR]

Background: Sepsis poses a serious threat to human life and health, with limited options for current clinical treatments. Acupuncture plays an active role in treating sepsis. However, previous studies have focused on the neuromodulatory effect of acupuncture, neglecting its network modulatory effect. Exosomes, as a new way of intercellular communication, may play an important role in transmitting acupuncture information. This paper explores the possibility of electroacupuncture-driven endogenous circulating serum exosomes and their carried miRNAs as a potential treatment for sepsis. Methods: The sepsis mouse model was established by intraperitoneal injection of lipopolysaccharide (LPS) (12 mg/kg, 24 mg/kg), and EA (continuous wave, 10 Hz, intensity 5) or intraperitoneal injection of Acupuncture Exosomes (Acu-exo) were performed before the model establishment. The therapeutic effect was evaluated by survival rate, ELISA, H&E staining and lung wet/dry weight ration (W/D). In vivo imaging of small animals was used to observe the accumulation of Acu-exo in various organs of sepsis mice. LPS was used to induce macrophages in cell experiments, and the effect of Acu-exo on macrophage inflammatory cytokines was observed. In addition, The miRNA sequencing method was further used to detect the serum exosomes of normal and EA-treated mice, and combined with network biology analysis methods to screen possible key targets. Results: EA and Acu-exo reduced the W/D and lung tissue damage in sepsis mice, down-regulated the expression of serum inflammatory cytokines TNF-α and IL-6, and increased the survival rate of sepsis mice. In vivo imaging of small animals found that Acu-exo were accumulated in the lungs of sepsis mice. Cell experiments proved that Acu-exo down-regulated the expression of inflammatory cytokines TNF-α, IL-6 and IL-1β to alleviate the inflammatory response induced by LPS in macrophages. MiRNA sequencing revealed 53 differentially expressed miRNAs, and network biology analysis revealed the key targets of Acu-exo in sepsis treatment. Conclusion: Electroacupuncture-driven endogenous circulating serum exosomes and their carried miRNAs may be a potential treatment for sepsis. [ABSTRACT FROM AUTHOR]

Existing research on integrated innovation primarily focuses on the integration of product functions or technologies in the engineering field, with limited exploration of cross-domain integration of biological knowledge. Various organisms exhibit various advantageous biological characteristics, and the multi-biological knowledge embedded within these characteristics can inspire designers to implement product integrated innovation. Biologically Inspired Design (BID) is a method that seeks inspiration from nature to achieve innovation in the engineering field. However, existing BID processes mainly apply the features of a single biological prototype. Against this background, this paper emphasizes how to achieve cross-domain integrated innovation through multi-biological knowledge. First, a method for generating integrated innovation ideas is proposed, achieved through obtaining biological prototypes and analyzing biological performance. Second, by obtaining the principal solution through the three mapping methods of biological effects, behaviors, and structures, a method for building the conceptual structure of integrated innovation is proposed. Finally, in conjunction with the Theory of Inventive Problem Solving (TRIZ), a product-integrated innovation design process model based on biological inspiration is constructed. The method is validated with an example of a six-legged firefighting robot. [ABSTRACT FROM AUTHOR]

A spiking neural network (SNN) is a type of artificial neural network that operates based on discrete spikes to process timing information, similar to the manner in which the human brain processes real-world problems. In this paper, we propose a new spiking neural network (SNN) based on conventional, biologically plausible paradigms, such as the leaky integrate-and-fire model, spike timing-dependent plasticity, and the adaptive spiking threshold, by suggesting new biological models; that is, dynamic inhibition weight change, a synaptic wiring method, and Bayesian inference. The proposed network is designed for image recognition tasks, which are frequently used to evaluate the performance of conventional deep neural networks. To manifest the bio-realistic neural architecture, the learning is unsupervised, and the inhibition weight is dynamically changed; this, in turn, affects the synaptic wiring method based on Hebbian learning and the neuronal population. In the inference phase, Bayesian inference successfully classifies the input digits by counting the spikes from the responding neurons. The experimental results demonstrate that the proposed biological model ensures a performance improvement compared with other biologically plausible SNN models. [ABSTRACT FROM AUTHOR]

In this paper, the generalized exponential (GExp) method has been employed to construct novel solitary wave solutions of the nonlinear fractional biological population (FBP) model. This model is used to demonstrate the relation of the population with deaths and births. Many novel traveling wave solutions have been formulated in distinct forms such as exponential, hyperbolic and trigonometric forms. These solutions have been explained in three different axes. The first axis is plotting them in their three optional (real, imaginary and absolute value), the second axis is handling these solutions for constructing the requested conditions for applying trigonometric quintic B-spline (TQBS) scheme. The second one determines the accuracy of the obtained analytical solutions by showing the error's value between the analytical and numerical solutions. At the same time, the third one is comparing our analytical and numerical solutions, which have recently been published that explain the paper's contribution and novelty. [ABSTRACT FROM AUTHOR]

Robust group lasso regression plays an important role in high-dimensional regression modeling such as biological data analysis for disease diagnosis and gene expression. However, most existing methods are optimized with prior variable structure knowledge under the traditional empirical risk minimization (ERM) framework, in which the estimators are excessively dependent on prior structure information and sensitive to outliers. To address this issue, we propose a new robust variable structure discovery method for group lasso based on a convergent bilevel optimization framework. In this paper, we adopt tilted empirical risk minimization (TERM) as the target function to improve the robustness of the estimator by assigning less weight to the outliers. Moreover, we modify the TERM objective function to calculate its Fenchel conjugate while maintaining its robust property, which is proven theoretically and empirically. Experimental results on both synthetic and real-world datasets show that the proposed method can improve the robust performance on prediction and variable structure discovery compared to the existing techniques. [ABSTRACT FROM AUTHOR]