Adaptive System Research Articles

Cytokines in gingivitis and periodontitis: from pathogenesis to therapeutic targets.

Chronic inflammatory processes in the oral mucosa and periodontitis are common disorders caused by microflora and microbial biofilms. These factors activate both the innate and adaptive immune systems, leading to the production of pro-inflammatory cytokines. Cytokines are known to play a crucial role in the pathogenesis of gingivitis and periodontitis and have been proposed as biomarkers for diagnosis and follow-up of these diseases. They can activate immune and stromal cells, leading to local inflammation and tissue damage. This damage can include destruction of the periodontal ligaments, gingiva, and alveolar bone. Studies have reported increased local levels of pro-inflammatory cytokines, such as interleukin-1beta (IL-1beta), tumor necrosis factor (TNF), IL-6, IL-17, and IL-23, in patients with periodontitis. In experimental models of periodontitis, TNF and the IL-23/IL-17 axis play a pivotal role in disease pathogenesis. Inactivation of these pro-inflammatory pathways through neutralizing antibodies, genetic engineering or IL-10 function has been demonstrated to reduce disease activity. This review discusses the role of cytokines in gingivitis and periodontitis, with particular emphasis on their role in mediating inflammation and tissue destruction. It also explores new therapeutic interventions that offer potential for research and clinical therapy in these chronic inflammatory diseases.

IFNh and IRF9 influence the transcription of MHCII mediated by IFNγ to maintain immune balance in sea perch lateolabrax japonicus

The major histocompatibility complex class II (MHCII) molecules are crucial elements of the adaptive immune system, essential for orchestrating immune responses against foreign pathogens. However, excessive expression of MHCII can disrupt normal physiological functions. Therefore, the host employs various mechanisms to regulate MHCII expression and maintain immune homeostasis. Despite this importance, limited studies have explored the negative regulation of MHCII transcription in bony fish. In this study, we found that interferon h (IFNh), a subtype of type I IFN in sea perch Lateolabrax japonicus, could inhibit the activation of IFNγ induced-MHCII expression by modulating the transcription of the class II major histocompatibility complex transactivator (CIITA). Transcriptome analysis revealed 57 up-regulated and 69 down-regulated genes in cells treated with both IFNγ and IFNh compared to those treated with IFNγ alone. To maintain cellular homeostasis, interferon regulatory factor 9 (IRF9) was up-regulated following IFNγ stimulation, thereby preventing MHCII overexpression. Mechanistically, IRF9 bound to the CIITA promoter and suppressed its expression activated by IRF1. Furthermore, IRF9 inhibited the promoter activity of both MHCII-α and MHCII-β induced by CIITA. Our findings highlight the roles of IFNh and IRF9 as suppressors regulating MHCII expression at different hierarchical levels. This study provides insights into the intricate regulation of antigen presentation and the foundation for further exploration of the interaction mechanisms between aquatic virus and fish.

Ensemble of artificial intelligence and physically based models for rainfall–runoff modeling in the upper Blue Nile Basin

ABSTRACT This study investigated the performance of the adaptive neuro-fuzzy inference system (ANFIS), feed forward neural network (FFNN), Soil and Water Analysis Tool (SWAT), Hydrologic Engineering Center's Hydraulic Modeling System (HEC-HMS), Hydrologiska Byråns Vattenbalansavdelning (HBV), and support vector regression (SVR) models for rainfall–runoff modeling using gauged and satellite rainfall, and their fusions in the Gilgel Abay watershed, Ethiopia. Afterward, simple average ensemble (SAE), weighted average ensemble (WAE), and neural network ensemble (NNE) techniques were applied to combine the outputs of individual models under three scenarios. The performance of the models was evaluated using Nash–Sutcliffe efficiency (NSE) and root mean square error (RMSE). The results demonstrated that the ANFIS model outperformed all the other single models with validation stage NSE values of 0.864 and 0.875, and RMSE values of 23.58 and 21.84 m3/s for gauge and fusion rainfall data, respectively. Among the physical-based models, SWAT gave better modeling performance with the validation stage NSE values of 0.81 and 0.821 for gauge and fusion rainfall data, respectively. Moreover, an ensemble of artificial intelligence and physical-based models greatly improved the overall modeling performance. The NNE improved the performance of single models up to 15.7 and 21.2 5% for fusion and satellite-based rainfall modeling, respectively.

New and future perspectives in familial Mediterranean fever and other autoinflammatory diseases

Systemic autoinflammatory diseases are a group of disorders characterized by sterile episodes of inflammation resulting from defects in the innate immune system. In contrast to classical autoimmune diseases, where circulating autoantibodies and the adaptive immune system are involved, these conditions involve excessive presence of proinflammatory cytokines leading to inflammatory attacks. Excessive cytokine production, functional mutations in regulatory pathways, excessive interferon production, defects in the nuclear factor-kappa B signaling pathway, abnorARCHmal protein folding, and complement activation are the mechanisms leading to autoinflammatory diseases. A defect in the mTOR pathway and trained immunity are newly discovered possible causes in pathogenesis. Early onset and severe forms of classical rheumatological diseases have been more frequently associated with autoinflammatory diseases in the last decade. Therefore, monogenic autoinflammatory diseases should be considered in rheumatic diseases with family history, consanguinity, early onset, and severe disease. The combination of functional and genotyping research will help to identify unclassified patients. The optimal treatment strategy remains uncertain, functional studies such as interferon signature and cytokine profiling, may prove valuable in guiding the treatment process. Stem cell transplantation strategies in autoinflammatory diseases with partial response to biological therapies can be considered. Autoinflammatory diseases are becoming increasingly complex and are bringing new perspectives to already known rheumatic diseases. Although we have effective treatments, we are still far from personalized recommendations.

Contribution of Artificial Intelligence (AI) in Education to Support the Achievement of Sustainable Development Goals (SDGs) 2030

This research aims to find out the contributions and threats of AI in education so that it can contribute to technological development in the SDGs 2030 era. The method in this research is qualitative using literature study. The contributions of AI in education include personal virtual tutors, adaptive learning systems, learning Chabot, game-based learning, AI based assessment systems, educational data analysis, automatic evaluation, accessibility and inclusiveness, assistants for learning, and helping to develop curriculum. The contribution of AI in education is very helpful for students to improve their skills and knowledge. It is expected that more will be able to participate in SDGs 4 (Quality Education). Threats of AI in education include data privacy and security, technology gaps, dependence on technology, fraud, and algorithm imperfections. Thus, there should be collaboration with AI experts to develop strategies and data simulations to minimize the threats.

The Phenotypes and Functions of Neutrophils in Systemic Sclerosis

Systemic sclerosis (SSc) is a chronic disease of the connective tissue characterized by its multifaceted impact on various bodily systems, yet its precise cause remains elusive. Central to its pathology are abnormal immune activation, vasculopathy, and consequent fibrosis affecting both the skin and internal organs. The intricate interplay between the innate and adaptive immune systems significantly influences the pathogenesis of SSc. Despite substantial research, the role of neutrophils, key players in innate immunity, in the context of SSc has remained enigmatic. Emerging evidence suggests that neutrophils not only contribute to the initiation and perpetuation of SSc but also inflict damage on organs and promote fibrosis—a hallmark of the disease in many patients. This review aims to investigate the nuanced involvement of neutrophils in the development of SSc. By shedding light on the intricate mechanisms through which neutrophils influence the pathogenesis of SSc, we can gain deeper insights into the disease process and potentially identify novel therapeutic targets. Understanding the precise role of neutrophils may pave the way for more targeted and effective interventions to alleviate the burden of SSc on affected individuals.

Heart brain axis in health and disease: role of innate and adaptive immunity.

The importance of the brain-heart interaction has been increasingly recognized as a critical physiological axis that is altered in disease. In this review, we explore the intricate relationship between the central nervous system and cardiovascular health, focusing particularly on immunological mechanisms that influence the course of both neurological and cardiovascular diseases. While previous studies have established a key role of the autonomic nervous system in linking brain and the heart, more recent studies have expanded our understanding of the multifaceted inter-organ interactions. As such, circulating mediators include immune cells of the adaptive and innate immune system and their secreted immunogenic factors have come into the focus as mediators along this bidirectional communication. Hence, in this review we briefly discuss the contribution of the autonomic nervous system and then focus on innate and adaptive immune mechanisms along the heart-to-brain and brain-to-heart axes, illustrating how cardiovascular diseases affect cognitive functions and how brain pathologies lead to cardiac complications.

Exploring the Structural Response of High-Rise Buildings using Fuzzy Logic and Genetic Algorithms

Exploring the dynamic behaviour of high-rise buildings presented a critical challenge. In this study, Fuzzy Logic and Genetic Algorithm (FLGA) were employed to comprehensively investigate the structural response of high-rise buildings (HRBs) for adaptive vibration control systems employing semiactive damper devices. The study integrated a fuzzy logic inference system with a Genetic Algorithm (GA) to create FLGA, which was then employed in a 3-story shear building under real earthquake conditions and multi-dimensional wind loads. The analytical model for a semi-active tuned mass damper (STMD) was examined and evaluated before integrating it into the proposed FLGA system. The results indicated that FLGA reduced story acceleration by an average of 20% and story drift by approximately 50% while efficiently controlling the structure within allowable movement limits and resident comfort criteria under multi-directional wind loads. The suggested optimal controller mitigated both displacement and acceleration responses by 15 to 20% without amplification. The study demonstrated FLGA's effectiveness as a model-free control strategy for managing structural response uncertainties.

Removal of Pb(II) pollution by algae-Fe-Ni magnetic nanocomposite

A magnetic nanocomposite material was synthesized by combining Chaetomorpha Algae with a Fe–Ni alloy. The dry algae, Fe, and Ni were mechanically alloyed using a ball milling technique in an argon atmosphere. The resulting Algae-Fe-Ni Magnetic Nanocomposite (AFNMN) was characterized using various analytical techniques, including particle size distribution (PSD) analysis, Brunauer-Emmett-Teller (BET) surface area analysis, scanning electron microscopy (SEM), differential scanning calorimetry (DSC), vibrating sample magnetometry (VSM), atomic absorption spectroscopy (AAS), and Fourier transform infrared (FTIR) spectroscopy. Multiple isotherm models were employed to study the adsorption of Pb(II) ions onto the AFNMN material. Density functional theory (DFT) was applied to predict the most stable amino acids for the removal of Pb(II) pollutants from a set of 14 amino acids. To optimize the Pb(II) uptake performance, three key experimental parameters were fine-tuned: pH, temperature, and reactor vessel geometry. Four different optimization methods were utilized, including response surface methodology (RSM), fuzzy logic, adaptive network-based fuzzy inference system (ANFIS), and artificial neural network genetic algorithms (ANN-GA). Additionally, a heat transfer simulation was conducted to determine the optimal vessel positions for achieving the desired temperature variations within the reactor during the adsorption process.

Age-associated contraction of tumor-specific T cells impairs antitumor immunity.

Progressive decline of the adaptive immune system with increasing age coincides with a sharp increase in cancer incidence. In this study, we set out to understand whether deficits in antitumor immunity with advanced age promote tumor progression and/or drive resistance to immunotherapy. We found that multiple syngeneic cancers grew more rapidly in aged versus young adult mice, driven by dysfunctional CD8+ T-cell responses. By systematically mapping immune cell profiles within tumors, we identified loss of tumor antigen-specific CD8+ T cells as a primary feature accelerating the growth of tumors in aged mice and driving resistance to immunotherapy. When antigen-specific T cells from young adult mice were administered to aged mice, tumor outgrowth was delayed and the aged animals became sensitive to PD-1 blockade. These studies reveal how age-associated CD8+ T-cell dysfunction may license tumorigenesis in elderly patients and have important implications for the use of aged mice as pre-clinical models of aging and cancer.

The function of antibodies.

Antibodies have multiple biological activities. They can both recognize and act on specific antigens. They can protect against and cause serious diseases, enhance and inhibit antibody responses, enable survival, and threaten life. Which among their many, often antagonistic properties explains that antibodies were selected half a billion years ago and transmitted to mammals across millions of generations? In other words, what is the function of antibodies? Here I examine how their structure endows antibodies with unique cognitive and effector properties that contribute to their multiple biological activities. I show that rather than specific properties, antibodies have large functional repertoires. They have a cognitive repertoire and an effector repertoire that are selected from larger available repertoires, themselves drawn at random from even larger virtual repertoires. These virtual repertoires provide the adaptive immune system with immense, constantly renewed, reservoirs of cognitive and effector functions that can be actualized at any time according to the context. I propose that such a flexibility, which enables living individuals to adapt to a rapidly changing environment, and even deal with an unknown future, may provide a better selective advantage than any particular function.

Quantum-Fuzzy Expert Timeframe Predictor for Post-TAVR Monitoring

This paper presents a novel approach to predicting specific monitoring timeframes for Permanent Pacemaker Implantation (PPMI) requirements following a Transcatheter Aortic Valve Replacement (TAVR). The method combines Quantum Ant Colony Optimization (QACO) with the Adaptive Neuro-Fuzzy Inference System (ANFIS) and incorporates expert knowledge. Although this forecast is more precise, it requires a larger number of predictors to achieve this level of accuracy. Our model deploys expert-derived insights to guarantee the clinical relevance and interpretability of the predicted outcomes. Additionally, we employ quantum computing techniques to address this complex and high-dimensional problem. Through extensive assessments, we show that our quantum-enhanced model outperforms traditional methods with notable improvement in evaluation metrics, such as accuracy, precision, recall, and F1 score. Furthermore, with the integration of eXplainable AI (XAI) methods, our solution enhances the transparency and reliability of medical predictive models, hence promoting improved clinical practice decision-making. The findings highlight how quantum computing has the potential to completely transform predictive analytics in the medical field, especially when it comes to improving patient care after TAVR.

Ontology, complex adaptive systems and economics

Ontology, complex adaptive systems and economics

Computational Methods for Predicting Key Interactions in T Cell-Mediated Adaptive Immunity.

The adaptive immune system recognizes pathogen- and cancer-specific features and is endowed with memory, enabling it to respond quickly and efficiently to repeated encounters with the same antigens. T cells play a central role in the adaptive immune system by directly targeting intracellular pathogens and helping to activate B cells to secrete antibodies. Several fundamental protein interactions-including those between major histocompatibility complex (MHC) proteins and antigen-derived peptides as well as between T cell receptors and peptide-MHC complexes-underlie the ability of T cells to recognize antigens with great precision. Computational approaches to predict these interactions are increasingly being used for medically relevant applications, including vaccine design and prediction of patient response to cancer immunotherapies. We provide computational researchers with an accessible introduction to the adaptive immune system, review computational approaches to predict the key protein interactions underlying T cell-mediated adaptive immunity, and highlight remaining challenges.

Heat transfer and erosion control of nanofluid flow with tube inserts: Discrete phase model and adaptive neuro-fuzzy inference system approach

The current research explores the heat transfer and erosion phenomena in a tube employing newly designed inserts. Seven different geometries for the inserts have been developed into three dimensions. The fluid used is water with iron oxide nanoparticles and the flow regime is turbulent. The modeling technique involves a finite volume method with the Eulerian-Lagrangian framework incorporating a particle-scale erosion model. The outcomes indicate that raising the velocity and volume fraction of nanoadditives enhance both the heat transfer and erosion. In comparison to the scenario without fins, the heat transfer can be increased by approximately 8.07, 6.22, 18.03, 53.72, 45.21, 14.74 and 13.08 for the considered cases G1, G2, G3, G4, G5, G6 and G7, respectively. The specific geometric design of the inserts significantly influences the heat transfer and erosion in the tube, with geometry G4 showing the most substantial improvements in the heat transfer (approximately 53 %) and erosion (around 100 %) compared to a smooth tube. Factors such as velocity, particle size, fluid viscosity and insert shape affect the erosion in the tube with water demonstrating higher erosion rates than oil. The introduction of iron oxide nanoparticles leads to 2–3 times increment in the tube wall erosion. The erosion estimation on the tube wall has been performed using an adaptive neuro-fuzzy inference system artificial intelligence model with R2 = 0.981.

Adaptive and dynamic smart traffic light system for efficient management of regular and emergency vehicles at city intersection

Adaptive and dynamic smart traffic light system for efficient management of regular and emergency vehicles at city intersection

Eco-friendly nanotechnology in rheumatoid arthritis: ANFIS-XGBoost enhanced layered nanomaterials

Rheumatoid arthritis (RA) is a chronic autoimmune disorder characterized by inflammation and pain in the joints, which can lead to joint damage and disability over time. Nanotechnology in RA treatment involves using nano-scale materials to improve drug delivery efficiency, specifically targeting inflamed tissues and minimizing side effects. The study aims to develop and optimize a new class of eco-friendly and highly effective layered nanomaterials for targeted drug delivery in the treatment of RA. The study's primary objective is to develop and optimize a new class of layered nanomaterials that are both eco-friendly and highly effective in the targeted delivery of medications for treating RA. Also, by employing a combination of Adaptive Neuron-Fuzzy Inference System (ANFIS) and Extreme Gradient Boosting (XGBoost) machine learning models, the study aims to precisely control nanomaterials synthesis, structural characteristics, and release mechanisms, ensuring delivery of anti-inflammatory drugs directly to the affected joints with minimal side effects. The in vitro evaluations demonstrated a sustained and controlled drug release, with an Encapsulation Efficiency (EE) of 85% and a Loading Capacity (LC) of 10%. In vivo studies in a murine arthritis model showed a 60% reduction in inflammation markers and a 50% improvement in mobility, with no significant toxicity observed in major organs. The machine learning models exhibited high predictive accuracy with a Root Mean Square Error (RMSE) of 0.667, a correlation coefficient (r) of 0.867, and an R2 value of 0.934. The nanomaterials also demonstrated a specificity rate of 87.443%, effectively targeting inflamed tissues with minimal off-target effects. These findings highlight the potential of this novel approach to significantly enhance RA treatment by improving drug delivery precision and minimizing systemic side effects.

High-precision, real-time wavefront sensing via sparse diffractive deep neural networks

As a key component of adaptive optics systems, wavefront sensing technology is an important way to effectively obtain aberrant phases in optical systems for high-capacity optical communications and high-quality imaging in relevant applications ranging from biological imaging to astronomical observation. To enhance the time efficiency of detection, the wavefront sensing with diffraction deep neural network (D2NN) directly calculates the wavefront information in the optical field. However, the compactness of the D2NN structure and the accuracy of wavefront prediction are important bottlenecks, restricting its practical application. Here, we design a multi-layer compact D2NN based on Bayesian optimization, called sparse D2NN (SD2NN), to achieve high-precision, real-time direct wavefront sensing. The experimental results demonstrated a reduction in the root-mean-square error (RMSE) of the SD2NN wavefront sensing of approximately 45.4%, along with a reduction in the axial length of approximately 82% in comparison to the unoptimized fully connected D2NN. This resulted in the attainment of a minimum layer distance of 8.77 mm. In addition, we additionally explored the effects of network depth and neuron size on the wavefront sensing performance of SD2NN and further summarized the general law of diffraction layer distance and neuron size. The proposed method will provide a reliable means of designing miniaturized integrated wavefront sensing chips.

Optimisation of the adaptive neuro-fuzzy inference system for adjusting low-cost sensors PM concentrations

Driven by the urgent necessity for accurate environmental data in urban settings, this research leverages the Adaptive Neuro-Fuzzy Inference System (ANFIS) as a machine learning-based approach to refine SPS30 low-cost sensor data influenced by hygroscopicity in Turin, Italy. Employing ANFIS offers several advantages: it enhances clarity regarding the correspondence between output and input values and rules, improves system interpretability, and facilitates the representation of linguistic variables and rules, thereby encouraging domain experts' involvement in enhancing the system's performance as needed. This paper illustrates the utility of ANFIS in adjusting the detected particulate matter (PM) concentration and compares its effectiveness with other established machine-learning techniques, including linear regression, decision trees, random forest, SVR and a multilayer perceptron (MLP). These methods are chosen as benchmarks owing to their established effectiveness in calibration procedures.We propose certain preprocessing steps for detecting and rectifying anomalies, alongside introducing two distinct data-splitting methodologies. Additionally, a discussion about feature selection is presented to elucidate the impact of specific features on performance enhancement. The efficacy of ANFIS in refining PM data is demonstrated through a comparative assessment, where it outperforms all the established machine-learning techniques. Notably, incorporating only PM2.5, relative humidity and temperature as features yields optimal performance while mitigating overfitting issues. The paper also explores various ANFIS configurations, including two distinct optimization algorithms, and investigates the impact of the number and type of membership functions on the fuzzy system's performance. Our study highlights the potential of the Adaptive Neuro-Fuzzy Inference System as a versatile and effective tool for addressing real-world challenges in environmental sensing.

Imaging the adsorption sites of organic molecules on metallic surfaces by an adaptive tunnelling current feedback.

Atomic force microscopy (AFM) allows submolecular resolution imaging of organic molecules deposited on a surface by using CO-functionalized qPlus sensors under ultrahigh vacuum and low temperature conditions. However, the experimental determination of the adsorption sites of these organic molecules requires the precise identification of the atomic structure of the surface on which they are adsorbed. Here, we develop an automation method for AFM imaging that provides in a single image both, submolecular resolution on organic molecules and atomic resolution on the surrounding metallic surface. The method is based on an adaptive tunnelling current feedback system that is regulated according to the response of the AFM observables, which guarantees that both the molecules and the surface atoms are imaged under optimum conditions. Therewith, the approach is suitable for imaging adsorption sites of several adjacent and highly mobile molecules such as 2-iodotriphenylene on Ag(111) in a single scan. The proposed method with the adaptive feedback system facilitates statistical analysis of molecular adsorption geometries and could in the future contribute to autonomous AFM imaging as it adapts the feedback parameters depending on the sample properties.