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Abstract Background Neural progenitor cells (NPCs) can be cultivated from developing brains, reproducing many of the processes that occur during neural development. They can be isolated from a variety of animal models, such as transgenic mice carrying mutations in amyloid precursor protein (APP) and presenilin 1 and 2 (PSEN 1 and 2), characteristic of familial Alzheimer’s disease (fAD). Modulating the development of these cells with inflammation-related peptides, such as bradykinin (BK) and its antagonist HOE-140, enables the understanding of the impact of such molecules in a relevant AD model. Results We performed a global gene expression analysis on transgenic neurospheres treated with BK and HOE-140. To validate the microarray data, quantitative real-time reverse-transcription polymerase chain reaction (RT-PCR) was performed on 8 important genes related to the immune response in AD such as CCL12, CCL5, CCL3, C3, CX3CR1, TLR2 and TNF alpha and Iba-1. Furthermore, comparative analysis of the transcriptional profiles was performed between treatments, including gene ontology and reactome enrichment, construction and analysis of protein-protein interaction networks and, finally, comparison of our data with human dataset from AD patients. The treatments affected the expression levels of genes mainly related to microglia-mediated neuroinflammatory responses, with BK promoting an increase in the expression of genes that enrich processes, biological pathways, and cellular components related to immune dysfunction, neurodegeneration and cell cycle. B2 receptor inhibition by HOE-140 resulted in the reduction of AD-related anomalies caused in this system. Conclusions BK is an important immunomodulatory agent and enhances the immunological changes identified in transgenic neurospheres carrying the genetic load of AD. Bradykinin treatments modulate the expression rates of genes related to microglia-mediated neuroinflammation. Inhibiting bradykinin activity in Alzheimer’s disease may slow disease progression.

Background: Neural progenitor cells (NPCs) can be cultivated from developing brains, reproducing many of the processes that occur during neural development. They can be isolated from a variety of animal models, such as transgenic mice carrying mutations in amyloid precursor protein (APP) and presenilin 1 and 2 (PSEN 1 and 2), characteristic of familial Alzheimer's disease (fAD). Modulating the development of these cells with inflammation-related peptides, such as bradykinin (BK) and its antagonist HOE-140, enables the understanding of the impact of such molecules in a relevant AD model. Results: We performed a global gene expression analysis on transgenic neurospheres treated with BK and HOE-140. To validate the microarray data, quantitative real-time reverse-transcription polymerase chain reaction (RT-PCR) was performed on 8 important genes related to the immune response in AD such as CCL12, CCL5, CCL3, C3, CX3CR1, TLR2 and TNF alpha and Iba-1. Furthermore, comparative analysis of the transcriptional profiles was performed between treatments, including gene ontology and reactome enrichment, construction and analysis of protein-protein interaction networks and, finally, comparison of our data with human dataset from AD patients. The treatments affected the expression levels of genes mainly related to microglia-mediated neuroinflammatory responses, with BK promoting an increase in the expression of genes that enrich processes, biological pathways, and cellular components related to immune dysfunction, neurodegeneration and cell cycle. B2 receptor inhibition by HOE-140 resulted in the reduction of AD-related anomalies caused in this system. Conclusions: BK is an important immunomodulatory agent and enhances the immunological changes identified in transgenic neurospheres carrying the genetic load of AD. Bradykinin treatments modulate the expression rates of genes related to microglia-mediated neuroinflammation. Inhibiting bradykinin activity in Alzheimer's disease may slow disease progression. [ABSTRACT FROM AUTHOR]

In contemporary biological research and applications, control systems have become indispensable for understanding and managing the intricate dynamics of the human biological system. Given the critical role of components such as the pancreas structure, protein formation, insulin and glucose regulation, and the genetic regulatory network (GRN), any disturbances in these systems can lead to severe health issues. To overcome these issues, to introduced a novel hybrid controller called the fuzzy lion‐based optimized fractional order system (FL‐OFOS) for evaluating the performance of the system. This controller aims to efficiently govern and regulate key components of the human biological system, including insulin dynamics, protein synthesis, pancreas functionality, and GRN management. The controller is specifically tailored to regulate insulin, protein synthesis, pancreas function, and GRN dynamics within the human biological system. The optimization of biological parameter values is achieved through the incorporation of the fuzzy lion function. The results of this study highlight the efficacy of the FL‐OFOS controller in optimizing and regulating various biological parameters. The system demonstrates minimal error rates, rapid response times, reduced overshoot, and high control precision and accuracy. The proposed controller achieves a minimal error rate of 0.98%, with only minor overshoot occurring in the outcomes. As a result, the FL‐OFOS controller offers substantial gains and delivers optimal results in the realm of biological systems. [ABSTRACT FROM AUTHOR]

Nowadays, the role of the control system in the bioengineering field plays a major part due to its significant improvement and demand. Usually, if there are any changes in the parameter of a biological system, then it tends to cause crucial diseases in humans. Previously, various control algorithms have been developed; however, the proper control process failed due to the high error rate, slow convergence, Computational Complexity, Tuning Complexity and inaccurate function. The proposed control strategy should ensure accurate tracking of the desired trajectory or reference signal, even in the presence of uncertainties and disturbances. The control system should be capable of adapting to changes in the system dynamics and maintaining stable operation. This complexity can lead to high computational requirements, making the controller computationally expensive and potentially unsuitable for real-time applications or systems with limited computing resources. To overcome these issues, in this work, the novel Hybrid Ant Lion Moth Flame Optimization (HALMFO) based Backstepping Fractional-Order Sliding mode Proportional-Integral-Derivative (BFO-SPID) controller is proposed for different nonlinear biological systems. The proposed method enhances the efficiency, improved performance, robustness, convergence speed, and solution quality of the optimization process. This paper addresses the challenge of controlling nonlinear biological systems by proposing a novel hybrid optimization-based backstepping approach combined with a fractional order sliding mode proportional-integral-derivative (PID) controller. This work's essential contribution lies in addressing existing control strategies' limitations by integrating multiple control techniques to achieve robust tracking performance and disturbance rejection. This system proposes predictive control with state estimation based on the backstepping sliding mode method. The gain parameters of the developed controller are optimized using the HALMFO method. The proposed control approach performance has been tested under biological systems such as Protein, Pancreas, and Genetic Regulatory Network (GRN). The execution of this proposed model is done in MATLAB/Simulink. Furthermore, the performance of the proposed control technique is compared with various state-of-the-art methods in terms of accuracy, Mean Average Error (MAE), Mean Square Error (MSE), settling time, overshoot, and rise time. The comparison illustrates that the projected controller has reached a steady condition with the least error rate in the control application. Thus the developed controller regulates the parameter variations in the biological system as per the desired level, providing significant effective outcomes to complex problems. [ABSTRACT FROM AUTHOR]

AbstractThis study presents the numerical solutions of the fractional schistosomiasis disease model (SDM) using the supervised neural networks (SNNs) and the computational scaled conjugate gradient (SCG), i.e. SNNs-SCG. The fractional derivatives are used for the precise outcomes of the fractional SDM. The preliminary fractional SDM is categorized as: uninfected, infected with schistosomiasis, recovered through infection, expose and susceptible to this virus. The accurateness of the SNNs-SCG is performed to solve three different scenarios based on the fractional SDM with synthetic data obtained with fractional Adams scheme (FAS). The generated data of FAS is used to execute SNNs-SCG scheme with 81% for training samples, 12% for testing and 7% for validation or authorization. The correctness of SNNs-SCG approach is perceived by the comparison with reference FAS results. The performances based on the error histograms (EHs), absolute error, MSE, regression, state transitions (STs) and correlation accomplish the accuracy, competence, and finesse of the SNNs-SCG scheme. [ABSTRACT FROM AUTHOR]

Within the scope of this investigation, educational instruments grounded on materialist ontology were employed to undertake a semantic analysis of the concepts of cell and the mechanisms of living organisms as they appear in biology and natural science textbooks. Drawing upon the ontological categories applied to distinct biological system levels, our findings demonstrate that the dominant approach for introducing the concept of cell and living organisms is via a holistic approach. However, prevalent inaccuracies relate to the presentation of tissue or organ properties, which neglect to reference the types of cells and their interactions responsible for emergent properties at this level. Our findings suggest that the use of a holistic approach to teach the functioning of living beings, may partially account for the difficulties when transitioning between levels of reality to construct explanations by students in this field of study. Our work provides tools to transition towards a biology education with a systemic approach to the content of cells and living organisms. [ABSTRACT FROM AUTHOR]

Molecular dynamics (MD) simulations are capable of reproducing dynamic evolution at the molecular scale, but are limited by temporal scales. Enhanced sampling has emerged as a powerful tool to improve sampling efficiency, thereby extending the simulation timescales of a range of simulation studies in materials, chemistry, biology, nanoscience, and related fields. Here, we provide a systematic overview of established enhanced sampling methods and clarify the principles and interconnections between these methods. Furthermore, we categorically elaborate on the state-of-the-art applications of enhanced sampling in the last five years. Through these exemplified applications, we discuss the unique advantages of this technique, showing the prospects and challenges for its future development. This review could help researchers in different fields gain a comprehensive understanding of the enhanced sampling technique, and jointly facilitate its application and advancement. [ABSTRACT FROM AUTHOR]

Bistability is a ubiquitous phenomenon in life sciences. In this paper, two kinds of bistable structures in two-dimensional dynamical systems are studied: one is two one-point attractors, another is a one-point attractor accompanied by a cycle attractor. By the Conley index theory, we prove that there exist other isolated invariant sets besides the two attractors, and also obtain the possible components and their configuration. Moreover, we find that there is always a separatrix or cycle separatrix, which separates the two attractors. Finally, the biological meanings and implications of these structures are given and discussed. [ABSTRACT FROM AUTHOR]

Copyright of Orientamenti Pedagogici is the property of Pontificio Ateneo Salesiano (Facoltà di Scienze dell'Educazione dell'Università Pontificia Salesian) 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.)

Computational fluid dynamics (CFD) is a part of fluid mechanics that uses numerical means and algorithms to anticipate and resolve problems associated with fluid flow (gas or liquid). Boundary conditions that are set, intends to mimic the clinical condition. Fluid flow dynamics in the various aspects of the biological systems such as blood flow can contribute significantly to the area of research and development. Alteration in the flow dynamics in relation to gas or liquid may have important effects over the biological systems involved. The present review article aims to shed light upon the fundamental aspects of the modelling the digital clinical model or a 3-dimensional model in order to know about the behaviour of the system concerned and to acquire knowledge for carrying out future experimentation which would be beneficial in scientific field for various treatment modalities. [ABSTRACT FROM AUTHOR]

The aim of this work is to present the numerical simulations of the novel designed fractional order Leptospirosis model (FOLM) by using the strength of stochastic numerical supervised neural networks. This novel work provides the numerical study of the Leptospirosis model, which is classified into five dynamics. Different values of the fractional order derivatives have been provided to solve the biological FOLM. The numerical formulations of the FOLM are obtained through the supervised neural networks (SNNs) along with the computational performances of the Levenberg-Marquardt backpropagation (LVMBP), i.e., SNNs-LVMBP. The correctness of the procedure is observed by using the comparative performances of the obtained and reference solutions. The statics are performed for these investigations as 74% and 13% for both certification and learning. The process of error histograms (EHs), recurrence, MSE, correlation, and state transitions (STs) will be performed to authenticate the capability, steadiness, accuracy, reliability, and fitness of the proposed procedure.

This study investigated how pre-service biology teachers perceived the concepts of 'system' – as the basic component of the systems thinking approach – and 'biological system' specific to biology. The most important characteristics of a system appear as elements, interactions, interconnections, hierarchy, purpose and forming a whole. The data of the study were collected from students who were studying at the Faculty of Education of a state university in Ankara. The study employed the basic qualitative research approach. A questionnaire consisting of open-ended questions and interviews was utilised to collect the data. The sample consisted of 150 pre-service teachers for the questionnaire and 15 pre-service teachers for the interviews. The collected data were subjected to thematic analysis. It was determined that the pre-service teachers had a general perception of a 'system' and a 'biological system', but this perception was not as strong as a perception that they could apply in their own educational processes. Systems thinking approach and system concept are critical not only for pre-service biology teachers but also for all other fields. It is considered that it is necessary to positively contribute to the perception of pre-service teachers who are the teachers of the future towards these concepts. [ABSTRACT FROM AUTHOR]

In this paper, the T-S fuzzy observer-based adaptive tracking control of the biological system with stage structure is studied. First, a biological model with stage structure is established, and its stability at the equilibrium points is analyzed. Considering the impact of reducing human activities on the biological population, an adaptive controller is applied to the system. Since it is difficult to measure density directly, a fuzzy state observer is designed, which is used to estimate the density of biological population. At the same time, the density of predators can track the desired density through the adjustment of adaptive controller. The stability of the biological system is guaranteed, and the observer error and tracking error are shown to converge to zero. Finally, the effectiveness of the proposed adaptive control method is verified by numerical simulation.

Introduction: Long non-coding RNAs (lncRNAs) and miRNAs belong to a class of non-coding RNAs (ncRNAs) that play important roles and functions in the regulation of the expression of genes in main biological processes, such as cell proliferation, apoptosis, and differentiation. LncRNAs can potentially affect miRNAs in the forms of cis/trans to modulate their regulatory role. In this study, mRNA, miRNA, and lncRNA gene networks were predicted by web-based programs for three ectodermal pathway markers (BMP4, NOG, FGF8) in the mouse embryonic stem cells. Material & Methods: In this theoretical bioinformatics study, the miRNAs of the target genes (BMP4, NOG, and FGF8) were extracted and examined by MirWalk and TARGETSCAN databases to finally obtain the common miRNAs of these three genes. Following that, the target lncRNAs for common miRNAs were then extracted from the DIANA-Tool database. (Ethic code: 100/21560/2/پ) Findings: MiRs mmu-miR-92a-2-5p, mmu-miR-129b-5p, mmu-miR-130b-5p, mmu-miR-692, mmu-miR-7009-3P, mmu-miR-7116-3p, and mmu-miR-7689-3p may affect the function of lncRNAs, including Kcnq1ot1, Gm26812, Gm4117, Gm11837, 4930423MO2Rik, Malat1, Gm12594, Gm3414, 5830444B04Rik, Gm2464, and NEAT1. Discussion & Conclusion: Due to the mutual relationships among lncRNA, miRNA, and mRNA, our results provided a novel perspective on lncRNAs for future research and experimental studies on ectodermal differentiation pathways and molecular mechanisms.

Bio-inspired computer modelling brings solutions fromthe living phenomena or biological systems to engineering domains. To overcome the obstruction problem of large-scale wind power consumption in Northwest China, this paper constructs a bio-inspired computer model. It is an optimal wind power consumption dispatching model of multi-time scale demand response that takes into account the involved high-energy load. First, the principle of wind power obstruction with the involvement of a high-energy load is examined in this work. In this step, highenergy load model with different regulation characteristics is established. Then, considering the multi-time scale characteristics of high-energy load and other demand-side resources response speed, a multi-time scale model of coordination optimization is built. An improved bio-inspired model incorporating particle swarm optimization is applied to minimize system operation and wind curtailment costs, as well as to find the most optimal energy configurationwithin the system. Lastly, we take an example of regional power grid in Gansu Province for simulation analysis. Results demonstrate that the suggested scheduling strategy can significantly enhance the wind power consumption level and minimize the system's operational cost. [ABSTRACT FROM AUTHOR]

The aim of this work is to present the numerical simulations of the novel designed fractional order Leptospirosis model (FOLM) by using the strength of stochastic numerical supervised neural networks. This novel work provides the numerical study of the Leptospirosis model, which is classified into five dynamics. Different values of the fractional order derivatives have been provided to solve the biological FOLM. The numerical formulations of the FOLM are obtained through the supervised neural networks (SNNs) along with the computational performances of the Levenberg-Marquardt backpropagation (LVMBP), i.e., SNNs-LVMBP. The correctness of the procedure is observed by using the comparative performances of the obtained and reference solutions. The statics are performed for these investigations as 74% and 13% for both certification and learning. The process of error histograms (EHs), recurrence, MSE, correlation, and state transitions (STs) will be performed to authenticate the capability, steadiness, accuracy, reliability, and fitness of the proposed procedure. [ABSTRACT FROM AUTHOR]

Threshold-dependent networks which behave like piecewise smooth systems and belong to a class of systems with discontinuous right hand side are studied with piecewise linear differential equations. At threshold values and their intersections, known as switching boundaries and surfaces, the state of such networks is not defined because of singularity at such points. This study characterises, in terms of number, singular domains of any order in a network and the total number of such domains; and also proposes new definitions for walls, using Filippov's First Order Theory on characterisation of (sliding) wall. The finding of this study is presented as propositions I, II and III respectively. In particular, using proposition II the study identified two white walls previously considered transparent. Introducing monotonicity to definition of transparent wall is also seen to affect qualitative dynamics like source, sink and cycles. [ABSTRACT FROM AUTHOR]

Copyright of Al-Academy is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) 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.)

In this paper, we understand the emergence of life as a pure individuation process. Individuation already occurs in open thermodynamics systems near equilibrium. We understand such open systems, as already recursively characterized (R1) by the relation between their internal properties, and their boundary conditions. Second, global properties emerge in such physical systems. We interpret this change as the fact that their structure is the recursive result of their operations (R2). We propose a simulation of the emergence of life in Earth by a mapping (R) through which (R1R2) operators are applied to themselves, so that RN = (R1R2)N. We suggest that under specific thermodynamic (open systems out of equilibrium) and chemical conditions (autocatalysis, kinetic dynamic stability), this mapping can go up to a limit characterized by a fixed-point equation: R = ϕ 1 ϕ 2 R . In this equation, ( ϕ 1 ) symbolizes a regime of permanent resonance characterizing the biosphere, as open from inside, by the recursive differential relation between the biosphere and all its holobionts. As such the biosphere is closed on itself as a pure differential entity. ( ϕ 2 ) symbolizes the regime of permanent change characterizing the emergence of evolution in the biosphere. As such the biosphere is closed on itself, by the principle of descent with modifications, and by the fact that every holobiont evolves in a niche, while evolving with it. [ABSTRACT FROM AUTHOR]

Abstract This research aims to discuss and control the chaotic behaviour of an autonomous fractional biological oscillator. Indeed, the concept of fractional calculus is used to include memory in the modelling formulation. In addition, we take into account a new auxiliary parameter in order to keep away from dimensional mismatching. Further, we explore the chaotic attractors of the considered model through its corresponding phase-portraits. Additionally, the stability and equilibrium point of the system are studied and investigated. Next, we design a feedback control scheme for the purpose of chaos control and stabilization. Afterwards, we introduce an efficient active control method to achieve synchronization between two chaotic fractional biological oscillators. The efficiency of the proposed stabilizing and synchronizing controllers is verified via theoretical analysis as well as simulations and numerical experiments.

Introduction: Long non-coding RNAs (lncRNAs) and miRNAs belong to a class of non-coding RNAs (ncRNAs) that play important roles and functions in the regulation of the expression of genes in main biological processes, such as cell proliferation, apoptosis, and differentiation. LncRNAs can potentially affect miRNAs in the forms of cis/trans to modulate their regulatory role. In this study, mRNA, miRNA, and lncRNA gene networks were predicted by web-based programs for three ectodermal pathway markers (BMP4, NOG, FGF8) in the mouse embryonic stem cells. Material & Methods: In this theoretical bioinformatics study, the miRNAs of the target genes (BMP4, NOG, and FGF8) were extracted and examined by MirWalk and TARGETSCAN databases to finally obtain the common miRNAs of these three genes. Following that, the target lncRNAs for common miRNAs were then extracted from the DIANA-Tool database. (Ethic code: 100/21560/2/پ( Findings: MiRs mmu-miR-92a-2-5p, mmu-miR-129b-5p, mmu-miR130b-5p, mmu-miR-692, mmu-miR-7009-3P, mmu-miR-7116-3p, and mmu-miR-7689-3p may affect the function of lncRNAs, including Kcnq1ot1, Gm26812, Gm4117, Gm11837, 4930423MO2Rik, Malat1, Gm12594, Gm3414, 5830444B04Rik, Gm2464, and NEAT1. Discussion & Conclusion: Due to the mutual relationships among lncRNA, miRNA, and mRNA, our results provided a novel perspective on lncRNAs for future research and experimental studies on ectodermal differentiation pathways and molecular mechanisms. [ABSTRACT FROM AUTHOR]

Liquid chromatography-mass spectrometry (LC-MS)-based lipidomics has been a subject of dramatic developments over the past decade. This review focuses on state of the art in LC-MS-based lipidomics, covering all the steps of global lipidomic profiling. On the basis of review of 185 original papers and application notes, we can conclude that typical LC-MS-based lipidomics methods involve: (1) extraction using chloroform/MeOH or MTBE/MeOH protocols, both with addition of internal standards covering each lipid class; (2) separation of lipids using short microbore columns with sub-2-μm or 2.6-2.8-μm (fused-core) particle size with C18 or C8 sorbent with analysis time

Electromagnetic fields (EMFs) are used to enable a number of modern devices, including mobile telecommunications infrastructure and phones, Wi-Fi, and Bluetooth. As radiofrequency EMFs at sufficiently high power levels can adversely affect human health. The question of the possible effects on human health due to exposure to electric and magnetic fields in the environment has been of interest for many years, especially because of the results of some research by the World Health Organization (WHO) that link the incidence of cancer, especially leukemia in children, living near overhead power distribution or transmission lines. There has been a considerable body of science addressing the relation between radiofrequency EMFs and adverse health outcomes, as well as significant developments in the technologies that use radiofrequency EMFs. This paper presents the measurements of the electric field in a 400 kV transformer station. The main purpose is to check does the limits of human exposure to electric fields given in the recommendations of ICNIRP - International Commission on Non-ionizing Radiation Protection in the Rulebook "Guidelines for limiting exposure to time-varying electric, magnetic and electromagnetic fields in frequency range from 0 to 300GHz ”since 1998 and the Council of the European Union and the preventive limits announced by WHO and IRPA for the whole world, in the case of a 400kV transmission line and transformer station, are fulfilled. [ABSTRACT FROM AUTHOR]

Applying systemic thinking to the resolution of complexity in biomimetic design allows technological integration between biological and man-made systems, thereby creating an autopoietic building organism. Emulating Nature in projects requires know-how cross-fertilization and hybridization. Mechanical solutions and those associated with the properties of materials are by no means the only options adaptive architecture can resort to in reproducing natural processes. This paper illustrates experiences that adopt adaptive autopoietic strategies at both urban and architectural product level. These strategies are typical of the natural world and involve the integration of technology and biology. The described experiences aim at highlighting the link between biomimetic design and the heteronomy of disciplines.