Biological Control Systems Research Articles

Do biological control agents adapt to local pest genotypes? A multiyear test across geographic scales.

Parasite local adaptation has been a major focus of (co)evolutionary research on host-parasite interactions. Studies of wild host-parasite systems frequently find that parasites paired with local, sympatric host genotypes perform better than parasites paired with allopatric host genotypes. In contrast, there are few such tests in biological control systems to establish whether biological control parasites commonly perform better on sympatric pest genotypes. This knowledge gap prevents the optimal design of biological control programs: strong local adaptation could argue for the use of sympatric parasites to achieve consistent pest control. To address this gap, we tested for local adaptation of the biological control bacterium Pasteuria penetrans to the root-knot nematode Meloidogyne arenaria, a global threat to a wide range of crops. We measured the probability and intensity of P. penetrans infection on sympatric and allopatric M. arenaria over the course of 4 years. Our design accounted for variation in adaptation across scales by conducting tests within and across fields, and we isolated the signature of parasite adaptation by comparing parasites collected over the course of the growing season. Our results are largely inconsistent with local adaptation of P. penetrans to M. arenaria: in 3 of 4 years, parasites performed similarly well in sympatric and allopatric combinations. In 1 year, however, infection probability was 28% higher for parasites paired with hosts from their sympatric plot, relative to parasites paired with hosts from other plots within the same field. These mixed results argue for population genetic data to characterize the scale of gene flow and genetic divergence in this system. Overall, our findings do not provide strong support for using P. penetrans from local fields to enhance biological control of Meloidogyne.

Bacterial chemotaxis control process analysis with SysML

AbstractThis paper looks at the bacteria chemotaxis control process utilizing the System Modeling Language (SysML) to leverage well‐defined and proven engineering tools for architecting, analyzing, and refining complex systems. It proposes a new methodology called reverse‐engineering object‐oriented systems engineering method (RE‐OOSEM) that converts descriptive biology research information into descriptive systems engineering information. It utilizes SysML and model‐based systems engineering (MBSE) to capture system architecture from biological system knowledge and inputs them into systems engineering tools. From an engineering point of view, this allows greater insight into how biological systems operate and suggests how much model detail is required to uncover a top‐down system understanding. RE‐OOSEM methodology guides the SysML chemotaxis control capture process. SysML syntax is used instead of biological syntax to facilitate biological chemotaxis control system analysis from an engineered system point of view. The model can act as a scaffolding to help uncover system function, the relationships of system components and processes, and bioinformatic phenotype and genotype correlation. An executable MathWorks Stateflow chemotaxis control process model based on the SysML architectural model is included. The results show the following engineering perspective observations. (1) Several control components are not dedicated but are available and utilized when needed. (2) Individual chemoreceptors act together as a sensor array. (3) Phosphate groups act as a signaling mechanism. (4) Methylation via CH3 groups of the chemoreceptor results in sensitivity adaptation. (5) Closed‐loop control collaboratively utilizes ligand bonding, phosphorylation, and methylation. (6) Timing relationships of the control subprocesses give insight into the system's architecture.

Ideal adaptive control in biological systems: an analysis of P\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathbb {P}$$\\end{document}-invariance and dynamical compensation properties

BackgroundDynamical compensation (DC) provides robustness to parameter fluctuations. As an example, DC enables control of the functional mass of endocrine or neuronal tissue essential for controlling blood glucose by insulin through a nonlinear feedback loop. Researchers have shown that DC is related to the structural unidentifiability and the P\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathbb {P}$$\\end{document}-invariance property. The P\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathbb {P}$$\\end{document}-invariance property is a sufficient and necessary condition for the DC property. DC has been seen in systems with at least three dimensions. In this article, we discuss DC and P\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathbb {P}$$\\end{document}-invariance from an adaptive control perspective. An adaptive controller automatically adjusts its parameters to optimise performance, maintain stability, and deal with uncertainties in a system.ResultsWe initiate our analysis by introducing a simplified two-dimensional dynamical model with DC, fostering experimentation and understanding of the system’s behavior. We explore the system’s behavior with time-varying input and disturbance signals, with a focus on illustrating the system’s P\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathbb {P}$$\\end{document}-invariance properties in phase portraits and step-like response graphs.ConclusionsWe show that DC can be seen as a case of ideal adaptive control since the system is invariant to the compensated parameter.

The Metabolic Syndrome, a Human Disease.

This review focuses on the question of metabolic syndrome (MS) being a complex, but essentially monophyletic, galaxy of associated diseases/disorders, or just a syndrome of related but rather independent pathologies. The human nature of MS (its exceptionality in Nature and its close interdependence with human action and evolution) is presented and discussed. The text also describes the close interdependence of its components, with special emphasis on the description of their interrelations (including their syndromic development and recruitment), as well as their consequences upon energy handling and partition. The main theories on MS's origin and development are presented in relation to hepatic steatosis, type 2 diabetes, and obesity, but encompass most of the MS components described so far. The differential effects of sex and its biological consequences are considered under the light of human social needs and evolution, which are also directly related to MS epidemiology, severity, and relations with senescence. The triggering and maintenance factors of MS are discussed, with especial emphasis on inflammation, a complex process affecting different levels of organization and which is a critical element for MS development. Inflammation is also related to the operation of connective tissue (including the adipose organ) and the widely studied and acknowledged influence of diet. The role of diet composition, including the transcendence of the anaplerotic maintenance of the Krebs cycle from dietary amino acid supply (and its timing), is developed in the context of testosterone and β-estradiol control of the insulin-glycaemia hepatic core system of carbohydrate-triacylglycerol energy handling. The high probability of MS acting as a unique complex biological control system (essentially monophyletic) is presented, together with additional perspectives/considerations on the treatment of this 'very' human disease.

Redox-enabled electronic interrogation and feedback control of hierarchical and networked biological systems

Microelectronic devices can directly communicate with biology, as electronic information can be transmitted via redox reactions within biological systems. By engineering biology’s native redox networks, we enable electronic interrogation and control of biological systems at several hierarchical levels: proteins, cells, and cell consortia. First, electro-biofabrication facilitates on-device biological component assembly. Then, electrode-actuated redox data transmission and redox-linked synthetic biology allows programming of enzyme activity and closed-loop electrogenetic control of cellular function. Specifically, horseradish peroxidase is assembled onto interdigitated electrodes where electrode-generated hydrogen peroxide controls its activity. E. coli’s stress response regulon, oxyRS, is rewired to enable algorithm-based feedback control of gene expression, including an eCRISPR module that switches cell-cell quorum sensing communication from one autoinducer to another—creating an electronically controlled ‘bilingual’ cell. Then, these disparate redox-guided devices are wirelessly connected, enabling real-time communication and user-based control. We suggest these methodologies will help us to better understand and develop sophisticated control for biology.

The strategy of optimal control for managing sugarcane shoot borer larvae

Pests and plant diseases pose significant challenges to Indonesia's agricultural development of sugar cane. The fall in national sugar productivity can be attributed, in part, to the presence of sugar cane shoot borer bugs. Managing sugar cane shoot borer pests is a crucial aspect of agriculture. The objective of this scientific study is to ascertain the optimal approach for the introduction of parasitoid larvae of sugarcane shoot borer pests. This will be done by looking at the Lotka-Volterra equation's stability and finding the best way to control it. This is a mathematical model used to show how biological control systems work. Specifically, the study focuses on the biological control of sugarcane shoot borer pests using their parasitic larvae, the Diatraeophaga striatalis Towns. The researchers opted for the qualitative descriptive approach, employing analytical tools derived from Miles and Huberman's four-stage framework. The equilibrium point and optimal control analyses show that the model presents various outcomes with distinct methods. These findings will be further examined in the subsequent sections of this paper.

Research progress in biological control of tomato bacterial wilt.

Bacterial wilt caused by the infection of Ralstonia solanacearum, is one of the most harmful diseases to tomatoes, one of the most important greenhouse vegetables in China. R. solanacearum can survive and remain active in the deep soil for a long time, and the chemical control of tomato bacterial wilt is consequently limited. In this study, we introduced the characteristics of tomato bacterial wilt disease and the types of R. solanacearum, and systematically reviewed the research progresses of biological control methods from the aspects of botanical insecticides, agricultural antibiotics, biocontrol bacteria. We emphatically introduced the principle and current status of these methods, discussed the limitations and the improvement strategies, and prospected a new environmental protection and efficient biological control system based on micro-ecological regulation would be the development direction of biological control of tomato bacterial wilt.

CYBERSECURITY ANALYSIS OF INDUSTRIAL CONTROL SYSTEMS

The current frontiers in the description and simulation of advanced physical and biological Industrial control systems (ICS) used to control various critical industrial and social systems. ICS integrates modern computing, communication, and Internet technologies. The integration of these technologies makes ICS open to the outside world, which makes it vulnerable to various cyber-attacks. ICS’s cybersecurity is becoming one of the most important issues due to the significant damage caused by cyberattacks to organizations and society. This article analyzes the cybersecurity issues of ICS. In particular, an analysis of the main components and architectures of the ICS, security aspects of the ICS, vulnerabilities, and threats to the cybersecurity of the ICS, as well as measures and means to ensure the cybersecurity of the ICS, is carried out. The analysis will help to give some insight into the cybersecurity issues of ICS and identify various research objectives necessary to ensure the cybersecurity of ICS.

Systems Engineering Approach to Modeling and Analysis of Chronic Obstructive Pulmonary Disease.

Chronic obstructive pulmonary disease (COPD) is a progressive lung disease characterized by airflow limitation. This study develops a systems engineering framework for representing important mechanistic details of COPD in a model of the cardiorespiratory system. In this model, we present the cardiorespiratory system as an integrated biological control system responsible for regulating breathing. Four engineering control system components are considered: sensor, controller, actuator, and the process itself. Knowledge of human anatomy and physiology is used to develop appropriate mechanistic mathematical models for each component. Following a systematic analysis of the computational model, we identify three physiological parameters associated with reproducing clinical manifestations of COPD: changes in the forced expiratory volume, lung volumes, and pulmonary hypertension. We quantify the changes in these parameters (airway resistance, lung elastance, and pulmonary resistance) as the ones that result in a systemic response that is diagnostic of COPD. A multivariate analysis of the simulation results reveals that the changes in airway resistance have a broad impact on the human cardiorespiratory system and that the pulmonary circuit is stressed beyond normal under hypoxic environments in most COPD patients.

Dynamics of a delay-induced prey–predator system with interaction between immature prey and predators

In biological pest control systems, several pests (including insects, mites, weeds, etc.) are controlled by biocontrol agents that rely primarily on predation. Following this biocontrol management ecology, we have created a three-tier prey–predator model with prey phase structure and predator gestation delay. Several studies have demonstrated that predators with Holling type-II functional responses sometimes consume immature prey. A study of the well-posedness and local bifurcation (such as saddle-node and transcritical) near the trivial and planer equilibrium points is carried out. Without any time lag, the prey development coefficient has a stabilizing impact, while increasing attack rate accelerates instability. Energy transformation rate and handling time are shown to cause multiple stability switches in the system. Numerical results demonstrate time delay is the key destabilizer that destroys stability. Our model can replicate more realistic events by including time-dependent factors and exploring the dynamic behavior of nonautonomous systems. In the presence of time delay, sufficient conditions of permanence and global attractivity of the nonautonomous system are derived. Finally, MATLAB simulations are performed to validate the analytical findings.

A Dynamic Control Center Based on a DNA Reaction Network for Programmable Building of DNA Nanostructures.

DNA-based nanostructures allow for complex self-assembly with nanometer precision through the specificity of Watson-Crick base pairing, but network behavior-directed control of the kinetic process is less studied. Here we show how the DNA reaction network (DRN), which has emerged as a reliable and programmable way to implement artificial network dynamics, can be built as the control center of programmable nanostructures, allowing spatiotemporal control over the dynamic behavior of DNA nanotubes. We chose a common network motif in biological control systems, the feed-forward loop, as the model network and demonstrated that dynamic behaviors, such as self-tuning control and multilayer hierarchical assembly, could be programmed by constructing an inhibition network and an excitation network, separately, in buffer solution and inside protocells.

From tensegrity to human organs-on-chips: implications for mechanobiology and mechanotherapeutics.

The field of mechanobiology, which focuses on the key role that physical forces play in control of biological systems, has grown enormously over the past few decades. Here, I provide a brief personal perspective on the development of the tensegrity theory that contributed to the emergence of the mechanobiology field, the key role that crossing disciplines has played in its development, and how it has matured over time. I also describe how pursuing questions relating to mechanochemical transduction and mechanoregulation can lead to the creation of novel technologies and open paths for development of new therapeutic strategies for a broad range of diseases and disorders.

BIOTECHNOLOGICAL STUDIES ON SALINITY-TOLERANT MICROORGANISMS.

Halophilic and halo-tolerant bacteria are micro-organisms that living in hyper saline environments. They have many potential in several fields in life such as Industry and agriculture. In this study a total of 17 isolates were obtained from the four saline ponds of Qarun Lake on Nutrient Agar (NA) medium prepared using ponds' water. The 16S rRNA analysis showed that our isolates affiliated with four groups, Firmicutes (47.06% of the isolates) that include family Bacillaceae with six different genera including Bacillus, Halobacillus, Lentibacillus, Oceanobacillus, Thalassobacillus and Virgibacillus, Gammaproteobacteria (35.29% of the isolates) including two genera Halomonas and Salinivibrio, two within Bacteriodetes group (11.76% of the isolates) and one of Alphaproteobacteria (5.88%). Firmicutes seems to be the more dominant group, including microorganisms of various genera, especially Bacillus (37.5%) followed by Halobacillus, Oceanobacillus, Lentibacillus and Virgibacillus (12.5% for each genera). 52.94% of isolates were gram negative, 35.29% were gram positive and 11.76% were gram variable. All isolates were catalase producers and motile. Halotolerance assay observed that only two isolates (11.76 %) were able to grow on the absence of salt and considered as halotolerant, while fifteen isolates (88.24%) were able to grow on NA media with different salt concentrations and considered as halophilic bacteria. Among them, thirteen isolates (76.47%) were able to grow on NA media with 7% NaCL. All isolates grew on NA with 12.5% NaCl; nine iso-lates (52.94%) were able to grow on NA media with 20% salinity, while three isolates (17.65%) appeared on NA media with salt concentration of 22% and considered extremely halophilic bacteria. All isolates were tested for plant growth promoting factors. The results showed that all isolates can produce IAA, while 47.06% of isolates can fix Nitrogen and 47.06 % have ability for phosphate solubilization. Concerning hydrolytic enzymes activi-ties of the isolates, it was found that 58.82% and 76.47% of isolates pro-duced lipase using olive oil and tween80 as a substrate respectively. It was also found that 47.06% of isolates showed ability for esterase production and same percentage for casease production. Gelatinase was excereted by 58.82%, while chitinase was produced by 70.59% of isolates. Five isolates were screened for biosurfactants and other bioactive compounds production. These isolates namly as QSLA1, QSLA7, QSLA14, QSLA16 and QSLA17 which were similar to, Halomonas sp.RS-17, Salinivibrio sp. pr6, Lentibacillus sp.strain BCHS25, Uncultured bacterium clone QAMU23 and Bacillus sp.strain6 in the GenBank database, respectively. Among them two isolates QSLA16 and QSLA17 had NRPs genes encoding for lipopeptide biosurfactants synthetase enzymes and HPLC analysis confirmed presence of different biosurfactants such as, surfactin, fengycin and mycosubtilin. The culture of the two isolates, QSLA1and QSLA7 were analysed by Gas chromatography–mass spectrometry (GC-MS) and showed ability for production of antifungal, antimicrobial and antitumer metabolites such as Desulphosinigrin, Undeca-2,4,6,8,10-pentaenal, 11-(2-furyl)-, oxime and Strychane, 1-acetyl-20à-hydroxy-16-methylene. Accordingly, QSLA1 had in vitro antibacterial activity against Salmonella typhi and Acinetobacter baumannii. Whereas, QSLA16 was able to inhibit the growth of bacterial pathogens Staphylococcus aureus, Klebsiella pneumonia and Salmonella typhi in contaminated salty whey. In this regard, QSLA1 showed in vitro antifungal activity against Alternaria solani and Fusarium oxysporium f.sp. lycopersici, and had in vivo a siginificant effect on the growth of Fusarium oxysporium f.sp. lycopersici infecting tomato seedlings compared to control and therefore can be used among biological control systems under saline conditions

Modularity in Nervous Systems—a Key to Efficient Adaptivity for Deep Reinforcement Learning

AbstractModularity as observed in biological systems has proven valuable for guiding classical motor theories towards good answers about action selection and execution. New challenges arise when we turn to learning: Trying to scale current computational models, such as deep reinforcement learning (DRL), to action spaces, input dimensions, and time horizons seen in biological systems still faces severe obstacles unless vast amounts of training data are available. This leads to the question: does biological modularity also hold an important key for better answers to obtain efficient adaptivity for deep reinforcement learning? We review biological experimental work on modularity in biological motor control and link this with current examples of (deep) RL approaches. Analyzing outcomes of simulation studies, we show that these approaches benefit from forms of modularization as found in biological systems. We identify three different strands of modularity exhibited in biological control systems. Two of them—modularity in state (i) and in action (ii) spaces—appear as a consequence of local interconnectivity (as in reflexes) and are often modulated by higher levels in a control hierarchy. A third strand arises from chunking of action elements along a (iii) temporal dimension. Usually interacting in an overarching spatio-temporal hierarchy of the overall system, the three strands offer major “factors” decomposing the entire modularity structure. We conclude that modularity with its above strands can provide an effective prior for DRL approaches to speed up learning considerably and making learned controllers more robust and adaptive.

Combatting insects mediated biotic stress through plant associated endophytic entomopathogenic fungi in horticultural crops.

Horticultural production is a vital catalyst for economic growth, yet insect infestations reduce horticultural crop yield and quality. Pesticides and other pest control methods are used during planting to eliminate pests that cause direct and indirect losses. In such situations, endophytic entomo-pathogenic fungi (EEPF) can act as a potential tools for biological control. They protect plants by boosting growth, nutrition, morpho-physiology and salt or iron tolerance. Antixenosis, antibiosis and plant tolerance change insect performance and preferences. EEPF- plant colonisation slows herbivore development, food consumption, oviposition and larval survival. EEPF changes plant physio-chemical properties like volatile emission profile and secondary metabolite production to regulate insect pest defences. EEPF produces chitinases, laccases, amylases, and cellulases for plant defence. Recent studies focused on EEPF species' significance, isolation, identification and field application. Realizing their full potential is difficult due to insufficient mass production, storage stability and formulation. Genetic-molecular and bioinformatics can help to build EEPF-based biological control systems. Metagenomics helps study microbial EEPF taxonomy and function. Multi-omics and system biology can decode EEPF interactions with host plants and microorganisms. NGS (Next Generation Sequencing), comparative genomics, proteomics, transcriptomics, metabolomics, metatranscriptomics and microarrays are used to evaluate plant-EEPF relationships. IPM requires understanding the abiotic and biotic elements that influence plant-EEPF interaction and the physiological mechanisms of EEPF colonisation. Due to restricted research, there are hundreds of unexplored EEPFs, providing an urgent need to uncover and analyse them.

Weed biological control with fungi-based bioherbicides

Biological control refers to the use of living beneficial organisms as well as the products of their metabolism in pest control. Weed plants are indispensable companions of cultivated plants, in which they cause substantial damage. Organic food production, human health care and environmental preservation impose a need for the production and application of bioherbicides, particularly in organic systems of plant production. Plant pathogens have significant potential as biological agents in weed control. The aim of the present study was to indicate the most important properties of the weed biological control system, with particular emphasis on the use of fungi-based bioherbicides. According to the organism they suppress, biopesticides are classified into bioinsecticides, biofungicides, bioherbicides, etc. Weed control using plant pathogens can be performed in three ways, by classical, conservation and augmentative biological control. Bioherbicides were initially introduced to the market in 1980, and the majority of them were fungi-based bioherbicides. The most common fungi included in bioherbicides belong to the genera Alternaria, Colletotrichum, Cercospora, Fusarium, Phomopsis, Phytophthora, Phoma, Puccinia, etc. The studies, development and final commercialisation of fungi as biological control agents face many obstacles, ranging from basic biological facts to social and economic factors. There are also challenges in the production, formulation process, environmental friendliness, duration of herbicidal action, and expensive and time-consuming registration procedures. Considering the success in weed suppression with fungi-based bioherbicides, the global market is still dominated by chemical companies manufacturing synthetic herbicides, while there are no such products on the Serbian market yet.

SYMBIOSIS OF MEDICAL TECHNOLOGIES AND ARTIFICIAL INTELLIGENCE: NEW OPPORTUNITIES IN ONCOLOGY

The OECI General Assembly (June 15–17, 2022, Valencia), during which the scientific conference “Artificial Intelligence: A Tool In Modern And Future Oncology” was held, clearly defined the vector of interests of the oncology for the coming years. A bright outburst of interest in artificial intelligence (AI) outside the scientific teams of cybernetic research and engineering laboratories took place against the background of numerous scientific and popular scientific publications, a number of world bestsellers and fantasy films.
 My fascination with the prospects of AI capabilities was formed after studying and analyzing a number of scientific publications and recently reading “AI Superpowers: China, Silicon Valley and The New World Order” by Kai-Fu Lee. Familiarity with the possibilities of AI allows us to realize that the surge of interest in its application in the medical field is due to the hope for the emergence of a “magic wand” that is capable of ensuring the emergence of new humanistic elements for the development of personalized medicine in the era of innovative technologies.
 The desire to create a virtual product to meet the fantastic needs of medicine permeates almost the entire history of the development of civilization. Even in prehistoric myths, fairy tales and legends, there was a desire to endow created objects or images with the qualities of intelligent subjects. Classical philosophical treatises of Aristotle, R. Descartes and others did not miss this topic; through their vision of a system of mechanical perception of thinking, they described the functional activity of a person. The originator and founder of modern cybernetics and science of control and communication, Norbert Wiener noted that both biological and mechanical control systems are aimed at the implementation of rational purposeful behavior, which in turn ensures the minimization of errors through the system of training and accumulated experience.
 Today, it can be said without a doubt that AI, after a difficult and winding path between fantastic optimism and excessive skepticism, has become an everyday part of our lives. Its intervention in the general human consciousness happened quickly and imperceptibly. Recently, this process has acquired a revolutionary character and has captured almost all spheres of human activity.
 A clear idea was formed in public conscience that innovations in the field of AI are able to provide the modern service market with new creative developments. It became clear that AI products based on the application of innovative technologies are capable of changing our lives in the near future. Thanks to AI, today it is possible to decipher the structure of any human protein and predict its change in the pathological process.
 At the beginning of the 21st century, world leaders in the field of information technology initiated the creation of a number of large-scale projects aimed at solving key problems of modern medicine, including existing and projected challenges in oncology.
 The birth of the symbiosis of biological and cybernetic sciences, aimed at processing information with the help of simulation programs, began to be perceived as both an object for research and a subject of the process of learning about the functioning of living things. Such capabilities allowed humanity to simultaneously process large volumes of structured and unstructured databases, generate significantly larger volumes of scientific information necessary for extrapolation of individual features and search for the closest analogue. In general, digital image processing in many areas of human activity has become one of the main directions of scientific and technical progress and is used to increase the efficiency and reliability of decisions made.
 At the start of this process, we should not only learn to set a specific task, but also try to understand the range of parameters and algorithms of the process in order to prevent choosing the wrong vector, which will deprive us of the desire to move forward.
 The combination of the capabilities of AI tools along with the quantitative and qualitative identification of molecular and structural-functional features of cellular components can be a good example of a successful symbiosis in optimizing the diagnosis of diseases and the choice of treatment.
 However, up-to-day, among the evidence-based methods of medical and biological examinations, cytomorphological studies remain the basis of cancer diagnostics.
 Solving the problem of improving the quality of research of morphological, histological, and immunohistochemical preparations can occur using computer data processing methods during the analysis of medical images, in particular computer aided diagnosis systems (CAD), which help specialists to interpret medical images. The imaging methods in X-ray, MRI, and ultrasound diagnostics, which have been actively developing since the late 70s, provide a significant additional amount of information that a medical professional analyzes and comprehensively evaluates in a short time. With the advent of whole-slide imaging and machine learning algorithms, CAD also has great potential for use in digital pathology for standard stained preparations. CAD systems process digital images of the entire slide and highlight areas of interest to offer the resulting data to support the decision made by the professional. The use of mathematical methods, computer data processing during the analysis of medical images allow solving the problems of accurate morphological and histological assessment of changes; automation of the processes of morphological research allows to significantly increase the efficiency of research work and achieve more accurate results.
 Today, with the use of digital analysis of histological and cytological images, extremely diverse tasks in the biological and medical fields are solved, and despite the existing limitations (the difficulty of obtaining a significant number of identical-quality histological specimens, the high variability of most histological structures), the automation of the microphoto processing allows for objective assessment of images, increased speed of information processing and the accuracy of detecting changes in cancer, and thereby expand the possibilities of histopathological research.
 In recent years, numerous experimental and clinical studies have shown that the difficulties of diagnosis, prognosis and treatment of cancer patients are associated with ambiguous clinical manifestations of the tumor process and differences in the morphogenetic characteristics of tumors caused by the etiopathogenetic diversity of neoplasias.
 In addition, it is known that the informativeness of biological objects on medical images obtained using optical and electron microscopy is insufficient due to the low contrast of the image of cells and cellular structures, the complexity of the biological organization of tissue structures, the presence in the field of view of various groups of cells, artifacts and significant heterogeneity of tissue as a background. Moreover, the technological equipment of laboratories, the provision of research with high-quality reagents play an important role as well as the human factor (the effectiveness of studying morphological, histological, immunohistochemical preparations, etc. which largely depends on the level of expertise, competence, and experience of the medical staff). It should also be noted that the processing of histological specimens is time-consuming and varies in the quality and types of protocols, methods and reagents in different laboratories, which can complicate the process of identifying morphological characteristics and reduce the accuracy and quality of research.
 That is why the improvement of cancer diagnosis and prognosis of the course of the disease largely depend on modern bioinformatics tools with the use of computer automation of image analysis, which is an extremely relevant and dynamically progressive approach. Various types of cluster analysis, learning algorithms of artificial neural network, artificial neural network methods, other mathematical methods as bioinformatics tools, are promising for performing the tasks of analyzing cytological, histological, immunohistochemical, immunocytological images in clinical practice.
 Today, in fact, new modern fields of research are emerging, the subject of which is the analysis of cytomorphological images (microphotos), such as digital pathology as image-based environment focused on study and and analysis of histological images, which includes image processing, data extraction (data mining) and database visualization, extraction, retrieval, comparison and management of biomedical knowledge within an array of image collections. In modern clinical practice and scientific laboratories, digital pathology is increasingly becoming a technological requirement of today. The advent of whole-slide imaging, faster networks, and low-cost storage solutions have made it easier for pathologists to manage and share medical digital images for clinical use. In parallel, progress in machine learning allows combining AI and digital pathology, offering new opportunities for accurate diagnosis based on molecular and ultrastructural cytological images.
 As for pattern recognition algorithms, a model trained on well-known described examples can be used to classify a new unfamiliar image. Machine learning algorithms applied to automated image classification also require a representative sample of image sets. Due to the high visual variability of histopathological images, a machine learning method usually needs a large number of images from different patients to make successful generalizations.
 Unfortunately, AI, having recognized all the key objects in the photo, cannot connect them by correct logic interpretation. Today’s task is the need to unite various “agents” of neural networks, to combine them into a single architectural complex that would reach the level, image and functionality in the imagination of the human intellect. Creating a modular architecture to optimize the functioning of a neural network is a common promising task for biologists and programmers to solve as a joint research team.
 The technological successes of such teams will allow AI to go beyond the boundaries of research laboratories and ensure the improvement of the quality of diagnostics and therapy. However, on the way to technology transfer, it is necessary to conduct thorough laboratory and clinical tests to protect potential users from misleading or insufficiently verified results. Organ-and-tissue and intracellular structural-and-molecular diversity requires careful data collection to train an effective and reliable algorithm.
 It is worth conducting an in-depth analysis of the identified errors and their differences in frequency and significance. At the same time, we should not forget that the biological system is not a constant, but is significantly dynamic. And here it is worth remembering the popular saying about the devil hiding in the details.
 Therefore, a superficial analysis of errors can rapidly change the balance of chances between life and death.
 It is this danger that forces a team of specialists to determine the limits of AI application in a timely manner, to identify indicators and protectors that could signal the limitation of its capabilities. AI algorithms provide high reliability only under the conditions of clear verification of quantitative and qualitative indicators of the pathological condition, and if possible, allow finding out their place in the hierarchy and architecture of the development of the malignant process.
 Deep fundamental research and analysis of the achieved successes and existing problems with the implementation of AI products obliges us to be careful so that one-sided rhetoric in rose-colored glasses around the phrase “pursuit in the field of artificial intelligence” does not destroy or once again slow down social processes close to Darwin’s evolutionary theory. We must promote scientific and technical symbiosis between representatives of the natural, cybernetic, engineering and technical fields without unnecessary excessive dominant ambitions and in compliance with legal norms and ethical standards. Convergence of ideas and goals, regarding the rapid symbiosis of modern medical technologies and AI in the interests of public values, usually requires new approaches and experiments on the part of the defined road.
 That is why specialists of various fields should not remain passive observers. We must seek and find a niche that will allow our ideas about the future of AI to provide solutions to the global problems of carcinogenesis and anticarcinogenesis. We must do everything to reduce the risks of the rapid spread of cancer, and offer the means of prevention, optimization of personalized diagnostics, monitoring of the course of the disease and effective therapy aimed at extending the duration and improving the quality of life of the patient.
 An epilogue to the editorial column will be a fitting quote from Steve Jobs in his famous stadium speech on June 12, 2005 to the students of Stanford University: “You can’t connect the dots looking forward; you can only connect them looking backwards. So you have to trust that the dots will somehow connect in your future.”
 One can only wish a fair wind to those who move …
 On this way, AI, as a director and moderator, will help to combine the interweaving of the hopes of patients and the crazy success of specialists, and will warn against unfortunate mistakes in the area of meeting the needs and capabilities of modern people.
 Vasyl Chekhun
 Editor-in-Chief of Experimental Oncology

A New Look at Young Children’s Referential Informativeness

In this article, I review experimental evidence for the dependence of 2- to 5-year-olds’ linguistic referential informativeness on cues to common ground (CG) and propose a process model. Cues to CG provide evidence for CG, that is, for the shared knowledge, beliefs, and attitudes of interlocutors. The presence of cues to CG (e.g., unimpeded listener line of regard or prior mention) is shown to be associated with less informative reference (e.g., pronouns). In contrast, the absence of cues to CG (e.g., impeded listener line of regard or new mention) is shown to be associated with more informative reference (e.g., nouns). Informativeness is sensitive to linguistic before nonlinguistic cues to CG (i.e., 2.0 vs. 2.5 years old, respectively). Reference is cast as a process of active inference, a formulation of Bayesian belief-guided control in biological systems. Child speakers are hierarchical generative models that, characteristically, expect sensory evidence for the evolved, prior Bayesian belief that interlocutor mental states are aligned (i.e., that CG exists). Referential control emerges as an embodied tool to gather evidence for this prior belief. Bottom-up cues to CG elicited by action drive updates to beliefs about CG. In turn, beliefs about CG guide efficient referential control.

Role of powdery mildew in the behavior of parasitoid: A case study using whiteflies and Encarsia formosa on tomato plants

The current study was conducted to assess the impact of powdery mildew (Oidium neolycopersici) on plant-non-vector herbivore-parasitoid interactions, e.g. tomato plants, whiteflies (Trialeurodes vaporariorum) and parasitoid (Encarsia formosa). Encarsia females were significantly less attracted to powdery mildew infected tomato plants (PM) than healthy plants (Con.). Thus parasitism rates of whiteflies (WF) were significantly lower on powdery mildew (PM) plants as compared to Con. plants (17.5:41.5%). These differences disappeared when E. formosa females were forced to parasitize whiteflies on either PM or Con. The performance of the parasitoid was not significantly influenced when developing on whiteflies feeding on powdery mildew infected plants. In olfactometer bioassays, either naïve or experienced parasitoids significantly preferred healthy plants colonised by whiteflies as compared to powdery mildew infected plants colonised by whitefly (PMWF) (30.0:10.0% for naïve parasitoids; 41.4:15.2% for experienced parasitoids). In wind tunnel bioassays, volatiles emitting from WFs significantly increased upwind oriented flight and landing in Encarsia targets as compared to any other treatments. Volatiles from Con, PM, PMWF and WF were collected and analyzed by GC and GC-MS, revealing that both qualities and quantities of volatile compounds differed to a large extend. Plants significantly increased volatile emission after being attacked by whiteflies (275.3 mol h−1) or infected by powdery mildew (267.58 mol h−1). However, plants significantly reduced volatile emissions in PMWF co-existing systems (80.58 mol h−1). Our results demonstrate that volatiles released by either herbivore damaged plants, pathogen diseased plants or combinations of these agents, respectively, may influence the foraging behavior of the parasitoids while searching for their hosts, resulting in reduced efficacies of parasitoids. This, in turn, may impact the outcome of biological control systems, especially in greenhouse conditions.

Behaviour drives contemporary evolution in a failing insect-parasitoid importation biological control programme

Biological control of pests continues to become more important in agriculture as pesticides are being withdrawn. However, successful control can be compromised by contemporary evolution. Recent work in New Zealand has shown that the once-successful biological control programme of the sexually reproducing grassland weevil pest Listronotus bonariensis by the asexual parasitoid Microctonus hyperodae has now failed. To explain the mechanisms associated with this, weevil parasitism rates were intensively monitored between 1994 and 2019. Frequent sampling took place at widely dispersed New Zealand sites spanning the warmer northern regions to the cooler south. Based on elapsed heat accumulation above the parasitoid’s development temperature threshold of 10.2°C degree-day (DD), the results over c. 25 years indicated that the extent of parasitism decline at a given location was directly related to the accumulated DD. The latter, in turn, was taken to be indicative of parasitoid activity and selection pressure. Accordingly, laboratory microcosm experiments measuring the response of weevils collected from the North–South distribution to a common population of parasitoids showed that the weevils from the warmer northern region showed higher rates of avoidance of the searching parasitoids than those from the cooler south. This strongly supported the hypothesis that the weevil resistance mechanism is related to levels of parasitoid avoidance behaviour arising from long-term parasitoid selection pressure. This study of the behaviourally based acquisition of resistance to a biological control agent illustrates a general need to consider the potential capability of an exotic target host to develop resistance to imported biological control agents. This includes identifying existing host adaptations that selection pressure could potentially act upon that may compromise otherwise successful biological control programmes. Such a requirement points to the need for long-term monitoring of biological control systems and understanding of parasitoid/host dynamics.