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The article discusses a study which investigates the short-term and long-term responses of circulating fish phagocytes peritoneal cavity and those found adhered to gills towards paper mill effluent exposure. It uses healthy fishes of Heteropneustes fossils of both sexes, divided into four groups. The three groups of fishes were exposed to different concentrations of paper mill effluent and the fourth group was exposed to zero effluent concentration. The researchers conclude that biological response of circulating fish phagocytes to an environmental pollutant depends on the type of pollutant and the level and duration of exposure.

An increasing interest in a healthy lifestyle raises questions about optimal body weight. Evidently, it should be clearly discriminated between the standardised "normal" body weight and individually optimal weight. To this end, the basic principle of personalised medicine "one size does not fit all" has to be applied. Contextually, "normal" but e.g. borderline body mass index might be optimal for one person but apparently suboptimal for another one strongly depending on the individual genetic predisposition, geographic origin, cultural and nutritional habits and relevant lifestyle parameters—all included into comprehensive individual patient profile. Even if only slightly deviant, both overweight and underweight are acknowledged risk factors for a shifted metabolism which, if being not optimised, may strongly contribute to the development and progression of severe pathologies. Development of innovative screening programmes is essential to promote population health by application of health risks assessment, individualised patient profiling and multi-parametric analysis, further used for cost-effective targeted prevention and treatments tailored to the person. The following healthcare areas are considered to be potentially strongly benefiting from the above proposed measures: suboptimal health conditions, sports medicine, stress overload and associated complications, planned pregnancies, periodontal health and dentistry, sleep medicine, eye health and disorders, inflammatory disorders, healing and pain management, metabolic disorders, cardiovascular disease, cancers, psychiatric and neurologic disorders, stroke of known and unknown aetiology, improved individual and population outcomes under pandemic conditions such as COVID-19. In a long-term way, a significantly improved healthcare economy is one of benefits of the proposed paradigm shift from reactive to Predictive, Preventive and Personalised Medicine (PPPM/3PM). A tight collaboration between all stakeholders including scientific community, healthcare givers, patient organisations, policy-makers and educators is essential for the smooth implementation of 3PM concepts in daily practice. [ABSTRACT FROM AUTHOR]

Presents abstracts of papers presented at the 31st Annual Meeting of the Association for Applied Psychophysiology and Biofeedback at the Adam's Mark Hotel in Denver, Colorado from March 29 to April 2, 2000. "Activation and Deactivation Dynamics in Response to Hyperventilation in Panic Disorder and Social Phobia," by Frank H. Wilhelm and Walton T. Roth; "The Comparative Effects of Book Bags and Carrying Styles on Upper Trapezius and Cervical Paraspinal Surface EMG," by Fred Shaffer, Elizabeth Malone, Tina Sippely and Christina Callahan; "Immune System Effects of Psychological Traits Which Affect Threat Perception," by Olafur S. Palsson and Stephanie L. Reavis.

A bioinspired approach to immune systems with a novel set of fuzzy rules is proposed in this paper to provide adaptability to sliding mode control. As multi-robotic systems have attracted the attention of researchers due to their variety of applications, as well as relevant control issues in practical demands, trajectory tracking and its extension to formation control are addressed. Despite its robustness, some disadvantages of first-order sliding mode control must be addressed, such as chattering and the need for a priori knowledge of the bounds of uncertainties and disturbances. Considering that an artificial immune system is a bioinspired approach provided with adaptive regulation mechanisms, this conception motivates the study and investigation of an adaptive strategy inspired by immunity. The leader–follower framework is used, and the nonholonomic differential-drive wheeled mobile robot model is considered. A fuzzy system based on the effect of the immunological reaction is designed, and an adaptation law is proposed to adjust the gain online. A fuzzy boundary layer method is also employed in replacement for the discontinuous control signal. Lyapunov's theorem proves the stability. The simulation and experimental results show the effectiveness of the proposed control strategy, which is also compared with another control scheme of recent research. [ABSTRACT FROM AUTHOR]

Mucosal melanoma (MM) is a rare and aggressive form of melanoma with a poorer prognosis compared to other subtypes. Recent large-scale next-generation sequencing studies, including our own research, have demonstrated that the molecular characteristics and potential oncogenic drivers of MM differ significantly from those of cutaneous melanoma. The emergence of selective CDK4/6 inhibitors, already approved for use in breast cancer and undergoing phase III clinical trials for other solid tumors, represents a promising development in the treatment of MM. Recent studies have shown that CDK4/6 inhibitors not only induce cell cycle arrest but also play a crucial role in facilitating the interaction between tumor cells and the host immune system. Moreover, our findings indicate that dysregulation of cell cycle progression due to cyclin‐dependent kinase 4 (CDK4) amplification is a significant genetic characteristic in a substantial portion of MM cases. Targeting CDK4 in specific MM patients shows promise for precision cancer therapy, utilizing molecularly characterized MM patient-derived xenograft (PDX) models and clinical trials. This paper provides an overview of existing literature on CDK4/6 dysregulation in MM, as well as preclinical and clinical investigations on CDK4/6 inhibitors and potential combination therapies for MM treatment. [ABSTRACT FROM AUTHOR]

In a bounded smooth domain Ω ⊂ R 3 and with a positive parameter χ > 0 , this paper is devoted to investigating the global boundedness and asymptotic behavior of solutions to a immune chemotaxis system. By establishing the uniform boundedness of L ∞ -norm for immune cells and the uniform boundedness of L q -norm with q ∈ (3 , ∞) for the chemokines gradient, it is possible to identify a smallness condition on chemosensitivity χ that ensures global existence of classical solution to the model in a three-dimensional bounded domain. Moreover, to get exponential decay rate the smallness of (‖ M 0 ‖ L ∞ , ‖ A 0 ‖ L ∞) is pivotal, we shall see that the system still admits an energy type inequality, and an analysis thereof will show that any nontrivial solution approaches exponentially fast with respect to the topology in L ∞ (Ω) . [ABSTRACT FROM AUTHOR]

The article reports a gut bacteria that could safeguard against a form of diabetes. This paper focuses on the autoimmunity occurring on the immune system attacking the body's cells and are responsible in various diseases including type 1 diabetes. This gut bacteria may build the connection between gut bacteria and the immune system of diabetics.

The article presents comments from cancer researcher, Todd Golub, on a paper about interfering with genes that reveal the cause of bone-marrow disorder. Golub stated that the condition, characterized by defective red blood-cell development, severe anaemia and a tendency to progress to acute myeloid leukaemia, had been linked to a gene somewhere in a 1.5-megabase common deleted region on chromosome 5, but they did not have the tools to identify the gene. However, things have changed since there emerged spectacular tools for the procedure such as RNA interference. A particularly persistent postdoc led to the discovery that the 5q-syndrome is caused by a defect in a gene called RPS14, which makes a ribosomal protein.

The article discusses the success of Erica Ollmann Saphire, an immunologist at the Scripps Research Institute in La Jolla, California, and her team in winning the race on providing a picture of how the deadly Ebola virus evades the immune system and enter cells. The move was risky and the work was long and difficult. It took them over four years to express 130 versions of the Ebola virus glycoprotein, grow and test crystal diffractions. The team found out that the virus can lie in wait for years. Saphire and her colleagues managed to solve the structure of an infection-blocking antibody bound to a viral protein, revealing a possible defence against the virus.

The article focuses on the efforts of co-director Alan Aderem and his colleagues in elucidating the complex networks that regulate the body's immune system. They are using both computational and experimental approaches, with each of the techniques informing and validating the others. The team has suspected that any transcription factors in the first set of genes would turn on genes in the second set, and transcription factors in that set would turn on genes in the third, and so forth.

Rotating machines are one of the most common equipments in modern industry, effective fault detection and diagnosis methods are vital to equipment health monitoring. In industrial production, the known information of fault types is insufficient generally, especially for constructing complex equipment and components. In previous studies of equipment fault detection, accurate fault classification and diagnosis methods have been presented, while seldom takes the condition of paucity of fault data into account. Therefore, this paper presents a novel antibody population optimization based artificial immune system (APO-AIS) for rotating equipment anomaly detection. The proposed approach can detect abnormal events while monitoring the operating condition. Meanwhile, an antigen-based antibody selecting method, a density-based antibody screening method and an optimized judgment rule based on individual difference are presented for improving the iteration evolution. The presented methods and optimized judgment rule enhance the robustness and reduces training burden for the proposed approach, which leads to accurate anomaly detection in strong background noise and in practical industrial environment. The effectiveness and robustness of the proposed method has been proven experimentally by bearing fault diagnosing and centrifugal pump condition monitoring in this paper. [ABSTRACT FROM AUTHOR]

Traditional network security detection models are trained offline using attack samples of known types. Although such models have high detection rates for known attack types, they cannot identify new attack types in the network layer. At present, these detection systems have the disadvantages of slow system construction and a high cost of model updating. Facing the increasing expansion of networks and endless attacks, these detection systems lack self-adaptability and expansibility, so it is difficult to detect complex and changeable attack events in networks. In this paper, the integrated use of immunology theory, complex adaptive system theory, and computational experiment technology is proposed to develop an Internet network layer security detection model based on an artificial immune system as an improvement over existing models of Internet network layer security. On the basis of testing knowledge, when a new type of attack is encountered, the online detection and learning process enables the dynamic extension of the network security detection model. Experimental calculations and an example analysis are presented to verify the scientific validity and feasibility of the model. [ABSTRACT FROM AUTHOR]

Time of day affects how well the immune system responds to viral or bacterial infections. While it is well known that the immune system is regulated by the circadian clock, the dynamic origin of time-of-day-dependent immunity remains unclear. In this paper, we studied the circadian control of immune response upon infection of influenza A virus through mathematical modeling. Dynamic simulation analyses revealed that the time-of-day-dependent immunity was rooted in the relative phase between the circadian clock and the pulse of viral infection. The relative phase, which depends on the time the infection occurs, plays a crucial role in the immune response. It can drive the immune system to one of two distinct bistable states, a high inflammatory state with a higher mortality rate or a safe state characterized by low inflammation. The mechanism we found here also explained why the same species infected by different viruses has different time-of-day-dependent immunities. Further, the time-of-day-dependent immunity was found to be abolished when the immune system was regulated by an impaired circadian clock with decreased oscillation amplitude or without oscillations. [ABSTRACT FROM AUTHOR]

Chemotherapy is a cornerstone of cancer treatment, but it can also induce immune suppression, which can have significant implications for patient outcomes. This review paper aims to give a general overview of how chemotherapy affects the immune system and how it affects cancer treatment. Chemotherapy can directly affect immune cells, leading to cytotoxic effects, cell differentiation and function alterations, and cell communication and signaling pathways disruptions. Such immune suppression can weaken the anti-tumor immune response and increase the risk of immune-related toxicities. Understanding the mechanisms of chemotherapy-induced immune suppression is crucial for optimizing treatment strategies. Strategies to mitigate immune suppression include immunomodulatory agents as adjuvants to chemotherapy, combination therapies to enhance immune function, and supportive care measures of the immune system. Additionally, identifying potential biomarkers to predict immune suppression and guide treatment decisions holds promise for personalized cancer medicine. Future directions in this field involve further elucidating underlying mechanisms, exploring novel combination therapies, and developing targeted interventions to minimize immune suppression. By understanding and addressing chemotherapy-induced immune suppression, we can optimize cancer treatment strategies, enhance the anti-tumor immune response, and improve patient outcomes. [ABSTRACT FROM AUTHOR]

Reaction systems (RSs) are a successful natural computing framework inspired by chemical reaction networks. A RS consists of a set of entities and a set of reactions. Entities can enable or inhibit each reaction and are produced by reactions or provided by the environment. In this paper, we define two quantitative variants of RSs: the first one is along the time dimension, to specify delays for making available reactions products and durations to protract their permanency, while the second deals with the possibility to specify different concentration levels of a substance in order to enable or inhibit a reaction. Technically, both extensions are obtained by modifying in a modular way the Structural Operational Semantics (SOS) for RSs that was already defined in the literature. Our approach maintains several advantages of the original semantics definition that were: (1) providing a formal specification of the RS dynamics that enables the reuse of many formal analysis techniques and favours the implementation of tools, and (2) making the RS framework extensible, by adding or changing some of the SOS rules in a compositional way. We provide a prototype logic programming implementation and apply our tool to three different case studies: the tumour growth, the Th cell differentiation in the immune system and neural communication. [ABSTRACT FROM AUTHOR]

In this paper, we study the following reaction-diffusion-advection system u t = D u Δ u - χ ∇ · (u ∇ v) + f (u) , (x , t) ∈ Ω × (0 , ∞) , v t = D v Δ v + s v u w - μ v v , (x , t) ∈ Ω × (0 , ∞) , w t = D w Δ w + s w - λ w u w - μ w w , (x , t) ∈ Ω × (0 , ∞) , in a smoothly bounded domain Ω ⊂ R n , which describes a directed movement of immune cells toward chemokines during the immune process, where D u , D v , D w , s v , s w , λ w , μ v , μ w , χ are positive parameters, and f ∈ C 1 ([ 0 , ∞)) is a kinetic function. When n ≥ 1 , if there exist positive constants α and θ 0 such that sup s ≥ 0 { f (s) + α s } < ∞ and lim s → ∞ inf - f (s) s 2 = : μ ∈ (θ 0 , ∞ ] , then the solution of the system is global and uniformly bounded. In particular, when n = 2 and f (0) ≥ 0 , the condition of f(u) could be improved as follows: if there exists α > 0 such that sup s ≥ 0 { f (s) + α s } < ∞ and one of the conditions that lim s → ∞ inf - f (s) ln s s 2 = : μ ∈ (2 χ s v C w D v , ∞ ] or 2 2 χ s v C GN 4 m 1 C w D v ≤ D u holds, then the solution of the system is still global and uniformly bounded, where m 1 is a positive constant given by below, C GN > 0 is the Gagliardo-Nirenberg inequality's constant and C w represents the uniform upper bound of w. Moreover, when f ≡ 0 and 2 2 χ s v C GN 4 m 1 C w D v ≤ D u , the global and uniform boundedness of solutions can also be established. [ABSTRACT FROM AUTHOR]

In this paper we address the issue of how to think about immunity. Many immunological writings suggest a straightforward option: the view that the immune system is primarily a system of defense, which naturally invites the talk of strong immunity and strong immune response. Despite their undisputable positive role in immunology, such metaphors can also pose a risk of establishing a narrow perspective, omitting from consideration phenomena that do not neatly fit those powerful metaphors. Building on this analysis, we argue two things. First, we argue that the immune system is involved not only in defense. Second, by disentangling various possible meanings of ‘strength’ and ‘weakness’ in immunology, we also argue that such a construal of immunity generally contributes to the distortion of the overall picture of what the immune system is, what it does, and why it sometimes fails. Instead, we propose to understand the nature of the immune system in terms of contextuality, regulation, and trade-offs. We suggest that our approach provides lessons for a general understanding of the organizing principles of the immune system in health and disease. For all this to work, we discuss a wide range of immunological phenomena. [ABSTRACT FROM AUTHOR]

In this paper, we propose a scheme for speeding up the generation of steady-state entanglement of two interacting qubits by coupling them with an engineered dissipative ancilla. The coupling strength between the target qubits and the ancilla is tailored by state-based Lyapunov control, which vanishes gradually along with the increasing of the fidelity of steady state. The negativity is adopted to measure the bipartite entanglement, which would not be enhanced by such Lyapunov control. In order to speed up and enhance the bipartite entanglement, we further use the non-vanishing engineered coupling. Numerical simulations indicate that the scheme is robust against the fluctuations of the coupling strength and immune to the system parametric errors. The study shows promising performance of speeding up dissipative generation of quantum states with Lyapunov control. [ABSTRACT FROM AUTHOR]

Aim: COVID-19 has led to a global crisis not only in the health sector but also in the economic, social and education sectors, as well as the lives of common man. This paper reviews the origin, characteristics and transmission of the SARS-CoV-2 Virus and the preventive measures adopted by the world's nations to curb the effects of the pandemic. Subjects and methods: The temporal and spatial profile of the COVID-19 spread is highly dynamic and is progressively changing at a rapid pace. This article is an attempt to mitigate the pandemic threat by sharing the review outcome from an extensive literature survey on recent articles on COVID-19 and its impact. Results: Statistical analysis presented by the World Health Organization and other health organizations explicitly indicates that optimized support and care extended to infected persons is the key to therapy, and the clinical effectiveness of alternative techniques is still under study. Though there have been announcements on the discovery of drugs for the virus, the authenticity of these reports are yet to be verified. Conclusion: Based on the review, it can be inferred that it is important to boost one's immune system and to stay healthy by developing healthy habits of food intake, sleep, exercise, personal hygiene and so on. Practicing social distancing with a self-motivated attitude will help to control the virus to a large extent. The current situation also demands a great sense of responsibility of people towards society by protecting oneself from the virus in order to protect the society one lives in. [ABSTRACT FROM AUTHOR]

Purpose: The immune system is influenced by many factors, including female sex hormones. The extent of this influence, however, is not completely understood so far. This systematic literature review aims at giving an overview of the existing concepts on how endogenous progesterone influences the female immune system along the menstrual cycle. Methods: The inclusion criteria were healthy female subjects in their reproductive age with a regular menstrual cycle. The exclusion criteria were exogenous progesterone, animal models, nonhealthy study populations and pregnancy. This led to 18 papers covered in this review. The search was performed using the databases EMBASE, Ovid MEDLINE and Epub, and the last search was conducted on September 18, 2020. Our findings were analyzed in four categories: cellular immune defense, humoral immune defense, objective and subjective clinical parameters. Results: We demonstrated that progesterone acts in an immunosuppressive way, favoring a Th-2-like cytokine profile. Further, we showed that progesterone inhibits mast cell degranulation and relaxes smooth muscle cells. Furthermore, we found supporting evidence for a so-called window of vulnerability after ovulation, where immune functions are lowered and mediated through progesterone. Conclusion: The clinical relevance of these findings is not completely understood yet. As the sample sizes of included studies were rather small and the content of them was broad, further investigations are needed to define to which extent the described changes actually clinically meaningful, whether they are capable of influencing the female health and how these findings can be used to increase well-being. [ABSTRACT FROM AUTHOR]

This paper focuses on the gut–lung axis in the context of Inflammatory Bowel Disease (IBD) and Chronic Obstructive Pulmonary Disease (COPD), highlighting the key role played by microbial dysbiosis and the impact of environmental and genetic factors on the innate and acquired immune system and on chronic inflammation in the intestinal and pulmonary tracts. Recent evidence indicates that Antigen-Presenting Cells (APCs) perform regulatory activity influencing the composition of the microbiota. APCs (macrophages, dendritic cells, B cells) possess membrane receptors known as Pattern Recognition Receptors (PRRs), a category of toll-like receptors (TLRs). PRRs recognise distinct microbial structures and microbial metabolites called Signals, which modulate the saprophytic microbial equilibrium of the healthy microbiota by recognising molecular profiles associated with commensal microbes (Microbe-Associated Molecular Patterns, MAMPs). During dysbiosis, pathogenic bacteria can prompt an inflammatory response, producing PAMPs (Pathogen-Associated Molecular Patterns) thereby activating the proliferation of inflammatory response cells, both local and systemic. This series of regulatory and immune-response events is responsible (together with chronic infection, incorrect diet, obesity, etc.) for the systemic chronic inflammation (SCI) known as "low-grade inflammation" typical of COPD and IBD. This review looks at immunological research and explores the role of the microbiota, looking at two recent clinical studies, SPIROMICS and AERIS. There is a need for further clinical studies to characterize the pulmonary microbiota and to obtain new information about the pathogenesis of lung disease to improve our knowledge and treatment strategies and identify new therapeutic targets. [ABSTRACT FROM AUTHOR]

In the recent years, various food additives, medicinal plants, and their bioactive components have been utilized in anti-inflammatory and immunomodulatory therapy. Nigella sativa is a key dietary supplement and food additive which has a strong traditional background. It is also one of the most broadly studied seeds in the global pharmaceutical and nutraceutical sector. N. sativa seeds are potential sources of natural metabolite such as phenolic compounds and alkaloids. The anti-inflammatory and immunomodulatory abilities of these seeds, most peculiarly with reference to some inflammatory and immune mediators, are reviewed. N. sativa and its bioactive compounds modulate inflammatory and immunomodulatory mediators including tumor necrosis factor-alpha (TNF-α), interferon gamma (IFN-γ), nuclear factor kappa B (NF-kB) cyclooxygenase (COX), lipoxygenase (LOX), transforming growth factor beta (TGF-β), interleukins, and immunoglobulin levels. This paper comprehensively describes the biomarkers and signaling pathways underlying the anti-inflammatory and immunomodulatory potential of N. sativa. This review also explains the scientific basis and the pharmacological properties of core bioactive ingredients of N. sativa responsible for these biological activities which indicates that their bioactive components could be possibly regarded as favorable therapy for disorders linked to inflammation and immune-dysregulation. [ABSTRACT FROM AUTHOR]

Drug Discovery is a lengthy and costly process and has faced a period of declining productivity within the last two decades resulting in increasing importance of integrative data-driven approaches. In this paper, data mining and integration is leveraged to inspect target innovation trends in drug discovery. The study highlights protein families and classes that have received more attention and those that have just emerged in the scientific literature, thus highlighting novel opportunities for drug intervention. In order to delineate the evolution of target-driven research interest from a biological perspective, trends in biological process annotations from Gene Ontology and disease annotations from DisGeNET are captured. The analysis reveals an increasing interest in targets related to immune system processes, and a recurrent trend for targets involved in circulatory system processes. At the level of diseases, targets associated with cancer-related pathologies, intellectual disability, and schizophrenia are increasingly investigated in recent years. The methodology enables researchers to capture trends in research attention in target space at an early stage during the drug discovery process. Workflows, scripts, and data used in this study are publicly available from https://github.com/BZdrazil/Moving%5fTargets. An interactive web application allows the customized exploration of target, biological process, and disease trends (available at https://rguha.shinyapps.io/MovingTargets/). [ABSTRACT FROM AUTHOR]

Humans are always exposed to the threat of infectious diseases. It has been proven that there is a direct link between the strength or weakness of the immune system and the spread of infectious diseases such as tuberculosis, hepatitis, AIDS, and Covid-19 as soon as the immune system has no the power to fight infections and infectious diseases. Moreover, it has been proven that mathematical modeling is a great tool to accurately describe complex biological phenomena. In the recent literature, we can easily find that these effective tools provide important contributions to our understanding and analysis of such problems such as tumor growth. This is indeed one of the main reasons for the need to study computational models of how the immune system interacts with other factors involved. To this end, in this paper, we present some new approximate solutions to a computational formulation that models the interaction between tumor growth and the immune system with several fractional and fractal operators. The operators used in this model are the Liouville–Caputo, Caputo–Fabrizio, and Atangana–Baleanu–Caputo in both fractional and fractal-fractional senses. The existence and uniqueness of the solution in each of these cases is also verified. To complete our analysis, we include numerous numerical simulations to show the behavior of tumors. These diagrams help us explain mathematical results and better describe related biological concepts. In many cases the approximate results obtained have a chaotic structure, which justifies the complexity of unpredictable and uncontrollable behavior of cancerous tumors. As a result, the newly implemented operators certainly open new research windows in further computational models arising in the modeling of different diseases. It is confirmed that similar problems in the field can be also be modeled by the approaches employed in this paper. [ABSTRACT FROM AUTHOR]

Virtual Traffic Lights (VTLs) come under the umbrella of Vehicular Adhoc Networks (VANETs) and are self-organized systems to implement traffic light control systems at a road intersection. During a VTL lifetime, VTLs may fail to maintain the uninterrupted working of the traffic light system. The vehicles arriving after a stable cluster formation and ongoing operation of VTL may go unattended which can weaken its application effect. If a new vehicle approaches the intersection, it either needs to get re-clustered with the current VTL cluster or has to form a new cluster with other arriving vehicles. In this paper, we aim to incorporate the Positive Selection Algorithm (PSA) scheme which is flourished with Artificial Immune System (AIS) behaviour to detect and control this anomalous clustering condition of the VTL traffic management systems. We propose the Adaptive Layer Positive Selection Algorithm (ALPSA) for monitoring the participating and non-participating vehicles; which are detected by appropriate detectors, generated through the algorithm. The algorithm works in layers and has two phases. Prior to the algorithm, we present a mobility model to generate the metrics for appropriate clustering. We extensively evaluate the ALPSA where results validate the management with reduced 'similarity detection time' and better 'similarity detection rate'. The similarity detection time is the response time given to the vehicles to decide whether to join the VTL cluster or not. Therefore, reduction in this time has shown better working for VTLs. [ABSTRACT FROM AUTHOR]

In general, medical pattern is a collection of characters formed using the characters such as 'a', 'c', 'g' and 't'. The length of the pattern varies from one disease to another disease, and the pattern also seems to be different for different patients. Identifying an unusual pattern from the information design is troublesome a decade ago. In this paper, with a specific end goal to enhance the precision of the anomalous distinguishing pattern, an Artificial Immune System (AIS) is framed. In the present study, AIS is used to obtain the abnormal pattern by learning the characteristics of the entire data set. Due to the powerful and adaptive nature of AIS, detecting and identifying abnormal pattern is more accurate. This proposed idea is implemented in MATLAB software and experimented on DNA/RNA dataset and the performance is verified. [ABSTRACT FROM AUTHOR]

In medicine, white blood cells (WBCs) play an important role in the human immune system. The different types of WBC abnormalities are related to different diseases so that the total number and classification of WBCs are critical for clinical diagnosis and therapy. However, the traditional method of white blood cell classification is to segment the cells, extract features, and then classify them. Such method depends on the good segmentation, and the accuracy is not high. Moreover, the insufficient data or unbalanced samples can cause the low classification accuracy of model by using deep learning in medical diagnosis. To solve these problems, this paper proposes a new blood cell image classification framework which is based on a deep convolutional generative adversarial network (DC-GAN) and a residual neural network (ResNet). In particular, we introduce a new loss function which is improved the discriminative power of the deeply learned features. The experiments show that our model has a good performance on the classification of WBC images, and the accuracy reaches 91.7%. Graphical Abstract Overview of the proposed method, we use the deep convolution generative adversarial networks (DC-GAN) to generate new samples that are used as supplementary input to a ResNet, the transfer learning method is used to initialize the parameters of the network, the output of the DC-GAN and the parameters are applied the final classification network. In particular, we introduced a modified loss function for classification to increase inter-class variations and decrease intra-class differences. [ABSTRACT FROM AUTHOR]

Current studies on security of IOT are mainly concentrated in the detection and control of behaviors of sensing nodes and neglect the protection of the key components of sensing nodes. So that sensor nodes may be destroyed and don't function when malicious behaviors are detected. To solve this problem, in this paper, we apply trusted computing to study the security of Internet of things and propose the trusted immune system of the sensing network. The trusted immune system not only just guarantee the trust of behaviors of sensing nodes, but also guarantee the trust of the key components of sensing nodes. By implementing immune surveillance, immune defense and immune homeostasis, the trusted immune system of the sensing network provides initiatively immune support for the sensing network combining with macro and micro. [ABSTRACT FROM AUTHOR]

Systemic lupus erythematosus (SLE) is an autoimmune disease associated with hyperactive innate and adaptive immune systems that cause dermatological, cardiovascular, renal, and neuropsychiatric problems in patients. SLE's multifactorial nature and complex pathogenesis present significant challenges in its clinical classification. In addition, unpredictable treatment responses in patients emphasize the need for highly specific and sensitive SLE biomarkers that can assist in understanding the exact pathogenesis and, thereby, lead to the identification of novel therapeutic targets. Recent studies on microRNA (miRNA), a non-coding region involved in the regulation of gene expression, indicate its importance in the development of the immune system and thus in the pathogenesis of various autoimmune disorders such as SLE. miRNAs are fascinating biomarker prospects for SLE categorization and disease monitoring owing to their small size and high stability. In this paper, we have discussed the involvement of a wide range of miRNAs in the regulation of SLE inflammation and how their modulation can be a potential therapeutic approach. [ABSTRACT FROM AUTHOR]

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Anomaly detection is an important issue, which has been deeply studied in different research domains and application fields. The dendritic cell algorithm (DCA) is one of the most popular artificial immune system inspired approaches to handle anomaly detection problems. The performance of DCA depends significantly on the parameters used to compute the relationship between input instance and detectors. However, we find that while the DCA's performance is good in practical applications, it is difficult to analyze due to the empirical based parameters and lacks adaptability. This paper studies how to effectively learn appropriate parameters for deterministic DCA (dDCA) for anomaly detection tasks. In particular, we propose a novel immune optimization based dDCA (IO-dDCA) for anomaly detection. It consists of dDCA classification, T cell (TC) classification, gradient descent optimization and immune nonlinear dynamic optimization. First, the dDCA is regarded as a binary classifier, and the data instances which are labeled as normal will be classified by a T cell inspired classification method, so as to improve the classification performance of dDCA. Then, to improve dDCA's adaptability, gradient descent is adopted for dDCA parameters' optimization. Finally, the immune nonlinear model is introduced to adjust learning rate in gradient descent to find the optimal parameters. The theoretical and experimental performance analysis of IO-dDCA show effectiveness of the novel approach through simulations, and the experimental results show that the proposed IO-dDCA has good classification accuracy. [ABSTRACT FROM AUTHOR]

Negative selection algorithm is the core algorithm of artificial immune system. It only uses the self for training and generates detectors to detect abnormalities. Holes are feature space areas that the detector fails to cover, it is the root cause of the performance degradation of the negative selection algorithm. The conventional method generates a large number of detectors randomly to repair the holes, which is time-consuming and not effective. To alleviate the problem, we propose a V-Detector-KN algorithm in this paper. V-Detector is the abbreviation of the real-valued negative selection algorithm with Variable-sized Detectors, KN represents Known Nonself. The V-Detector-KN algorithm uses the known nonself as the candidate detector to further generate the detector based on the V-Detector randomly generated detector, so as to realize the repair of holes. Compared with the conventional method to randomly generate detectors to repair holes, our proposed V-Detector-KN method uses known nonself to repair holes, reducing the randomness and blindness of hole repair. Theoretical analysis shows that the detection rate of our algorithm is not lower than that of the conventional V-Detector algorithm. The results of experiment comparing with other 6 algorithms on 7 UCI data sets show the superiority of our proposed algorithm. [ABSTRACT FROM AUTHOR]

Abstract??In this position paper, we argue that the field of artificial immune systems (AIS) has reached an impasse. For many years, immune inspired algorithms, whilst having some degree of success, have been limited by the lack of theoretical advances, the adoption of a naive immune inspired approach and the limited application of AIS to challenging problems. We review the current state of the AIS approach, and suggest a number ofchallengesto the AIS community that can be undertaken to help move the area forward. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a central disease with high incidence, and its pathological process is closely associated with changes of some biological indicators in the periphery. Among them, the intestinal flora mainly causes a series of pathological changes such as inflammation through the immune system, which may contribute to the pathological process of AD. In this paper, we mainly focused the relationship between gut microbiota and immune system disorder in the neuropathology of AD, underlining the significance of the advanced mechanism of inflammatory response and providing a new direction for the treatment of AD. [ABSTRACT FROM AUTHOR]

This paper develops a new algorithm by applying the Bayesian method to software using artificial immune systems. An artificial immune system is an adaptive computing system that uses models, principles, mechanisms, and functions used to solve problems in theoretical immunology. Its application to various fields of science is studied. The role that artificial immune systems play in software is invaluable. Methods for detecting malware are explored. Some works in the field of artificial immune system are analyzed and issues to be addressed are identified. The Bayesian method accurately calculates the probability of occurrence of any event under certain conditions. Therefore, the Bayesian method is applied to software using artificial immune systems. By applying this method, fast software performance can be achieved. For this, a new algorithm is developed and experiments are conducted. The developed algorithm is one of the new ones. The results of the experiments provide good performance. [ABSTRACT FROM AUTHOR]

In this article, the author focuses on the history of clustered regularly interspaced short palindromic repeats (Crispr-Cas system) that led to the discovery of prokaryotes' immune system with reference to several papers published by researchers in previous years. Topics discussed include characterization of Crisprs, project explanation on Mycobacterium tuberculosis, Crisprs' spacer sequences in conjugative plasmid origin and bacteriophage.

Artificial immune system constructs a dynamic and adaptive information defense system through a function similar to the biological immune system. In order to resist the external invasion of useless and harmful information and ensure the effectiveness and the harmlessness of received information. Due to the low accuracy and the high false positive rate of the existing clonal selection algorithms applied to intrusion detection, in this paper, we propose an improved clonal selection algorithm. The improved method detects the intrusion behavior by selecting the best individual overall and cloning them. Experimental results show that the improved algorithm achieves very good performance when applied to intrusion detection. And it is shown that the algorithm is better than BP neural network with its 99.5 % accuracy and 0.1 % false positive rate. [ABSTRACT FROM AUTHOR]

We social animals must balance the need to avoid infections with the need to interact with conspecifics. To that end we have evolved, alongside our physiological immune system, a suite of behaviors devised to deal with potentially contagious individuals. Focusing mostly on humans, the current review describes the design and biological innards of this behavioral immune system, laying out how infection threat shapes sociality and sociality shapes infection threat. The paper shows how the danger of contagion is detected and posted to the brain; how it affects individuals' mate choice and sex life; why it strengthens ties within groups but severs those between them, leading to hostility toward anyone who looks, smells, or behaves unusually; and how it permeates the foundation of our moral and political views. This system was already in place when agriculture and animal domestication set off a massive increase in our population density, personal connections, and interaction with other species, amplifying enormously the spread of disease. Alas, pandemics such as COVID-19 not only are a disaster for public health, but, by rousing millions of behavioral immune systems, could prove a threat to harmonious cohabitation too. [ABSTRACT FROM AUTHOR]

Cytokine pathways and their signaling disorders can be the cause of onset and pathogenesis of many diseases such as autoimmune diseases and COVID-19 infection. Autoimmune patients may be at higher risk of developing infection due to the impaired immune responses, the use of immunosuppressive drugs, and damage to various organs. Increased secretion of inflammatory cytokines and intolerance of the patient's immune system to COVID-19 infection are the leading causes of hospitalization of these patients. The content used in this paper has been taken from English language articles (2005–2020) retrieved from the PubMed database and Google Scholar search engine using "COVID-19," "Autoimmune disease," "Therapeutic," "Pathogenesis," and "Pathway" keywords. The emergence of COVID-19 and its association with autoimmune disorders is a major challenge in the management of these diseases. The results showed that the use of corticosteroids in the treatment of autoimmune diseases can make diagnosis and treatment of COVID-19 more challenging by preventing the fever. Due to the common pathogenesis of COVID-19 and autoimmune diseases, the use of autoimmune drugs as a possible treatment option could help control the virus. Key Points: • Inflammatory cytokines play an essential role in the pathogenesis of COVID-19 • ACE2 dysfunctions are related to the with COVID-19 and autoimmune diseases • The use autoimmune diseases drugs can be useful in treating COVID-19 [ABSTRACT FROM AUTHOR]

Cancer cell possesses numerous adaptations to resist the immune system response and chemotherapy. One of the most significant properties of the neoplastic cells is the altered lipid metabolism, and consequently, the abnormal cell membrane composition. Like in the case of phosphatidylcholine, these changes result in the modulation of certain enzymes and accumulation of energetic material, which could be used for a higher proliferation rate. The changes are so prominent, that some lipids, such as phosphatidylserines, could even be considered as the cancer biomarkers. Additionally, some changes of biophysical properties of cell membranes lead to the higher resistance to chemotherapy, and finally to the disturbances in signalling pathways. Namely, the increased levels of certain lipids, like for instance phosphatidylserine, lead to the attenuation of the immune system response. Also, changes in lipid saturation prevent the cells from demanding conditions of the microenvironment. Particularly interesting is the significance of cell membrane cholesterol content in the modulation of metastasis. This review paper discusses the roles of each lipid type in cancer physiology. The review combined theoretical data with clinical studies to show novel therapeutic options concerning the modulation of cell membranes in oncology. [ABSTRACT FROM AUTHOR]

In the present paper, we discuss the importance of the microbiome in allergic disease. In this review paper, the data from the Medline (PubMed) and search engine of Kirikkale University were systematically searched for all relevant articles in June 15th, 2015 for the past 30 years. The keywords of 'microbiome', 'dysbiosis', 'allergy', 'allergic rhinitis', 'allergic disease', 'mechanisms' and 'treatment' were used alone or together. In this paper, microbiomes were presented in terms of 'Definition', 'Influence of \the human microbiome on health', 'The microbiome and allergic diseases', and 'Modulation of the gut microbiota in terms of treatment and prevention'. Microbiological dysbiosis is also reviewed. The microbiome is the genetic material of all microbes (bacteria, fungi, protozoa, and viruses) that live on or in the human body. Microbes outnumber human cells in a 10:1 ratio. Most microbes live in the gut, particularly the large intestine. Changes in the immune function of the respiratory tract are (at least in theory) linked to the immunomodulatory activity of the gut microbiota via the concept of a 'common mucosal response'. The gut microbiota shapes systemic immunity, thus affecting the lung mucosa. Alternatively, changes in the gut microbiota may reflect alterations in the oropharyngeal microbiota, which may in turn directly affect the lung microbiota and host immune responses via microaspiration. Dysbiosis is defined as qualitative and quantitative changes in the intestinal flora; and modern diet and lifestyle, antibiotics, psychological and physical stress result in alterations in bacterial metabolism, as well as the overgrowth of potentially pathogenic microorganisms. All immune system components are directly or indirectly regulated by the microbiota. The nature of microbial exposure early in life appears to be important for the development of robust immune regulation; disruption of either the microbiota or the host response can trigger chronic inflammation. Dysbiosis is also an important clinical entity. Antibiotics, psychological and physical stress, and dietary factors contribute to intestinal dysbiosis. [ABSTRACT FROM AUTHOR]

This paper presents two new approaches to solve the reconfiguration problem of electrical distribution systems (EDSs) with variable demands, using the CLONALG and the SGACB algorithms. The CLONALG is a combinatorial optimization technique inspired by biological immune systems, which aims at reproducing the main properties and functions of the system. The SGACB is an optimization algorithm inspired by natural selection and the evolution of species. The reconfiguration problem with variable demands is a complex combinatorial problem that aims at identifying the best radial topology for an EDS, while satisfying all technical constraints at every demand level and minimizing the cost of energy losses in a given operation period. Both algorithms were implemented in C++ and test systems with 33, 84, and 136 nodes, as well as a real system with 417 nodes, in order to validate the proposed methods. The obtained results were compared with results available in the literature in order to verify the efficiency of the proposed approaches. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

Background subtraction is an essential step in the video monitoring process. Several models have been proposed to differentiate background pixels from foreground pixels. However, most of these methods fail to distinguish them in highly dynamic environments. In this paper, we propose a new method robust and more efficient for distinguishing moving objects from static objects in dynamic scenes. For this purpose, we propose to use a bio-inspired approach based on the Artificial Immune Recognition System (AIRS) as a classification tool. AIRS separates antibodies, represented by the pixels of the background model, from the antigens that model foreground pixels representing moving objects. Each pixel is modeled by a feature vector containing the attributes of a Gaussian. Only the pixels classified as background are taken into account by the system and updated in the model. This combination has allowed to benefit from two advantages: the power of AIRS to provide an online update of system parameters and the ability of Gaussians to adapt to scene variations at the pixel level. To test the proposed approach, six videos representing the dynamic background category of the CDnet 2014 dataset are selected. Obtained results proved the effectiveness of this new process in terms of quality and complexity compared to other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Robotics are effectively utilized in the field of speech therapy because, most of the people affected by the apraxia speech problem which is one of the neurological disorder. This disorder occurred due to the Alzheimer disease, stroke that affects the movement of mouth severely. These neurological disease affected people requires more attention while training the speech therapy. Traditional approaches are fail to manage the accuracy while making training also consumes more time to get result. So, in this paper introduces the metaheuristic optimized algorithm called artificial cuckoo immune system method to examine the mouth posterior which helps to provide the speech therapy with the help of robotics. This process helps to perform therapy session in both partial as well as automatic manner. The efficiency of robotic speech therapy process is analyzed using MATLAB based experimental process. During this process introduced system ensures 96.8% of accuracy, 97.1% of precision value, 96.9% of recall value and 98.2% of F1-score value. [ABSTRACT FROM AUTHOR]

In this paper, we establish and examine a mathematical model that combines the effects of vitamins intervention on strengthening the immune system and its role in suppressing and delaying the growth and division of tumour cells. In order to accomplish this, we propose a tumour–immune–vitamins model (TIVM) governed by ordinary differential equations and comprised of two populations, namely tumour and immune cells. It is presumed that the source of vitamins in TIVM originates from organic foods and beverages, based on the food pyramid. The simulation of TIVM employs the fourth order Runge–Kutta method. It is found from the analysis and simulation results that one of the side effects of weakening the immune system is the possibility of transforming immune cells into immune cancer cells to prevent or delay the growth and division of tumour cells. Evidently, for regular intakes of vitamins, which is projected at 55% of vitamins per day, the immune system is strengthened, preventing the production of tumour cells. [ABSTRACT FROM AUTHOR]

Porcine enteric coronaviruses (CoVs) cause highly contagious enteric diarrhea in suckling piglets. These COV infections are characterized by clinical signs of vomiting, watery diarrhea, dehydration, and high morbidity and mortality, resulting in significant economic losses and tremendous threats to the pig farming industry worldwide. Because the clinical manifestations of pigs infected by different CoVs are similar, it is difficult to differentiate between the specific pathogens. Effective high-throughput detection methods are powerful tools used in the prevention and control of diseases. The immune system of piglets is not well developed, so serological methods to detect antibodies against these viruses are not suitable for rapid and early detection. This paper reviews various PCR-based methods used for the rapid and efficient detection of these pathogenic CoVs in swine intestines. Key points: Swine enteric coronaviruses (CoVs) emerged and reemerged in past years. Enteric CoVs infect pigs at all ages with high mortality rate in suckling pigs. Rapid and efficient detection methods are needed and critical for diagnosis. [ABSTRACT FROM AUTHOR]

Mobile ad hoc network (MANET) is a collection of mobile, decentralized and self-organizing nodes that are used in special cases such as medical and military purposes. Securing mobile ad hoc network MANET is a crucial research issue. The properties of MANET impede the protection of the network's environment against attacks. MANET as an open area of wireless mobile nodes allow external attackers to join the network easily and masquerade legitimate nodes. Therefore, the objective of this paper is to develop a distributed, self-organizing and hybrid intelligent model, called dendritic cell fuzzy algorithm (DCFA), for security routing in MANET. The DCFA model inspiring the detection functionality of dendritic cells (DCs) in human immune system (HIS) and the accurate decision-making functionality of fuzzy logic theory to detect network attacks in MANET. The DCFA model is developed and validated by detecting a flooding-based attack, namely, a resource consumption attack (RCA). QualNet v5.0.2 is used as a simulation environment to test the capability of DCFA in detecting RCA over MANET. The results show the capability of DCFA to perform the detection operation with high efficiency and effectiveness. [ABSTRACT FROM AUTHOR]