Thе book presents the algorithms that ensure the maintenance of a constant false alarm rate (CFAR detectors) in the detection of radar targets in conditions of an intensively noisy environment. This type of detectors uses an adaptive threshold dependent on the disturbance level, thus ensuring that a constant false alarm probability is kept. The rapid development of algorithms in this area in recent years can be explained both by the very pronounced need in various fields of science and technology to automatically perform the procedure of detecting a useful signal against the background of disturbances, and by the theoretical interest to interpret the classical approaches to the detection tasks in a modern way.

Electromyography (EMG) is a fundamental tool in diagnosing neuromuscular disorders (NMD). Due to the complex nature of EMG signals, different approaches, based on artificial intelligence and machine learning, were developed for EMG signal analysis and NMD diagnosis. Considering the critical role of maximum voluntary contraction (MVC) as a fundamental metric in assessing muscle fatigue, in this work, classification of simulated surface EMG (sEMG) into MVC levels is performed. Unlike previous studies, which focus primarily on binary classification of fatigue and non-fatigue states, our approach employs a deep convolutional neural network for the classification of sEMG signals into ten MVC levels, where the model outputs categorical predictions, with each class representing a specific MVC level. sEMG signals were generated using a computer muscle model that we developed using MATLAB, which allows for greater control over variability, ensuring robustness and generalizability of the model. The obtained results demonstrate that the model achieved high performance in differentiating between the ten classes (MVC levels), with an accuracy, F1-score, recall, and precision of 88.88%, 88.75%, 88.80% and 88.86%, respectively. These findings reveal that the model can accurately differentiate across MVC levels, indicating a potential method for accurate assessment of muscle fatigue intensity.

This paper investigates the performance of various Electromagnetic Field Optimization (EFO) algorithms. Chaos maps are proposed to improve the performance of EFO algorithms. Ten chaotic maps are incorporated in EFO – Chebyshev, Circle, Gauss, Iterative, Logistic, Piecewise, Sine, Singer, Sinusoidal and Tent. To compare the performance of the constructed EFO algorithms, a case study of the identification of the model parameters of a cultivation process model is studied. An experimental data set from E. coli BL21(DE3)pPhyt109 fed-batch cultivation process is used. Based on the results of 30 runs of each EFO, some statistical and InterCriteria analyzes are performed. As a result, the best performing EFO algorithms are iterative EFO and tent chaotic map EFO. These algorithms gave the best objective value (best and mean value) and had a good distribution of results.

Dynamic programming (DP) is an elegant way to solve problems related to optimization and optimal control of processes. DP, however, has one major drawback, namely the “curse of dimensionality”. To overcome this shortcoming, an approach called neuro-dynamic programming (NDP) has been developed. This approach solves the “curse of dimensionality” problem of DP. For this purpose, a neural network is used in NDP, which ignores the poor results of the utility criterion. In this way, the time for solving the specific task is significantly shortened. In this work, an NDP algorithm is presented for the optimal control of a fed-batch biotechnological process for the production of L-lysine by the strain Brevibacterium flavum 22LD. Application of the NDP algorithm ensures maximum productivity of the L-lysine.

Eight single (SGA) and eight multi-population (MGA) genetic algorithms (GA) differing in the sequence of implementation of the main genetic operators’ selection, crossover and mutation, or omitting the mutation operator, have been examined for the purposes of parameter identification of a Saccharomyces cerevisiae fed-batch fermentation process model. The influence of some of the main genetic algorithm parameters, namely number of individuals, maximum number of generations, generation gap, crossover and mutation rates for both SGA and MGA, and insertion and migration probability for MGA only, have been investigated in depth. Almost all applied SGA and MGA led to similar values of the optimization criterion but the SGA with operators’ sequence mutation, crossover and selection, and MGA with operators’ sequence crossover, selection and mutation, are significantly faster than others while keeping the model accuracy. Among the considered GA parameters, generation gap influences most significantly to SGA and MGA convergence time, saving of about 40% of computational time of the algorithms without affecting the model accuracy.

Infections with vancomycin-resistant Enterococcus (VRE) have been increasingly reported, especially in immunocompromised patients. Bacteriophages (phages) are often considered as potential therapeutic candidates in treating infectious diseases. To search for therapeutic phages as antimicrobial agents for the treatment of Enterococcus faecium infections, eight E. faecium phages were isolated from the environments. One of these, phage φEc-ZZ2, which was capable of eliciting efficient lysis of E. faecium, was selected for analysis. Transmission electron microscopy revealed its resemblance to members of the family Siphoviridae, with a tail 200 nm long and an icosahedral head 45 nm in diameter. Phage φEc-ZZ2 exhibited rapid adsorption (> 99% adsorbed in 10 min), a short latency period of 20 min and a large burst size of 180 PFU/cell. Host range determination shows that φEc-ZZ2 has a relatively broader host range (specificity for 54.5% of tested E. faecium strains). Furthermore, when φEc-ZZ2 alone was incubated at different pH, the phage was stable over a wide pH range (5 to 9). These characteristics of φEc-ZZ2 will provide useful information for further research on the interaction between Enterococcus phages and their hosts. Moreover, in consideration of its pH stability and broader host range, φEc-ZZ2 may be a good candidate as a therapeutic/disinfectant agent to control nosocomial infections caused by E. faecium.

Pathogens of the Burkholderia genus are causing diseases in a diverse variety of hosts. After the discovery of T6SS, it was found to play a pivotal role in virulence and other pathogenicity factors in different pathogenic Burkholderia species. For this study, three strains of Burkholderia cenocepacia were selected from different ecological niches; J2315 from humans, MC0-3 from the rhizosphere of maize, and YG-3 from the Populus tree. The sequenced genomes were retrieved from PATRIC. It was found that B. cenocepacia J2315 and MC0-3 strains had only 1 cluster of T6SS in their genomes while the YG-3 strain had 3 clusters. The circular genomic map and phylogenetic tree suggested major differences in T6SS clusters 2 and 3 of the YG-3 strain from other clusters. From the results obtained in the study and reviewing the literature, it was concluded that all 3 strains harbor T6SS-1 type cluster that is involved in causing virulence in eukaryotic organisms and several bacterial species. This factor of causing virulence in the bacteria species might be helpful for B. cenocepacia strains J2315, MC0-3 and YG-3 in survival and niche adaptation.

The most common cancer in paediatric age is acute lymphoblastic leukaemia (ALL) which accounts for nearly a quarter of all cases of paediatric cancer. ALL cases are classified as B-cell or T-cell precursor ALL, based on their immunophenotypical features. Patients with T-cell ALL (T-ALL) comprise 10–15% of all newly diagnosed cases and are more prevalent in boys and in older age compared to the overall incidence peak age of ALL. In this case report, we present the morphometric and nanomechanical features of T-lymphoblasts derived from an 11-month-old infant with a rare subtype of γδ T-ALL with aggressive biological behavior. To investigate these features, we employed atomic force microscopy (AFM) and compared the blast cell deviations found in their nanostructure and elasticity to those of normal lymphocytes from a healthy child. The malignant T-lymphoblasts exhibited reduced roughness and Young’s modulus values. This single case analysis demonstrates the potential of the AFM method to provide additional information regarding the characteristics of malignant cells and suggests its potential as a complementary approach for distinguishing neoplastic cells from normal cells. The application of AFM could potentially facilitate the introduction of in vitro tests to determine the efficacy of anti-leukemic treatments.

To date, effective medication against hepatitis B virus (HBV) has not been developed. MicroRNAs (miRNAs) comprise a promising therapeutic approach to inhibit the virus. In this study, 1917 miRNAs in the miRBase database were screened using bioinformatics software to obtain candidates that can target HBV genotype B. Two parameters, namely pairing pattern and minimum free energy were used to select the qualifying miRNAs. Three miRNAs targeting the X gene and one miRNA targeting the C gene were identified out of 39 initial candidates. Uniquely, miR-6770-5p was the only candidate that could target the X gene of all HBV genotypes, with a higher potency of inhibition compared to other candidates. The three other candidates also showed good potency for some genotypes; thus, the identified candidates show promise as therapeutics for hepatitis infection.