The cultivated apple (Malus domestica Borkh.) is an economically important fruit crop in countries worldwide, including Bosnia and Herzegovina (BIH).The gene bank activities in BIH were initiated in the 1930s and continued until the war in the 1990s, when much of the documentation was lost. Since then, uncoordinated efforts were made to establish apple collections in different regions, but a comprehensive analysis of genetic resources was lacking. This prompted the current study where we present the first thorough overview of the national genetic resources of BIH apples. Thus, we analyzed 165 accessions in the apple gene bank at the Institute for Genetic Resources (IGR) established at Banja Luka using the 20 K apple Infinium® single nucleotide polymorphism (SNP) array. We combined the results with previously published data on the germplasm collections at Srebrenik and Goražde, genotyped using the Axiom® Apple 480 K SNP array. In total, 234 accessions were included in the study of which 220 were presumed to be local cultivars and 14 were known international reference cultivars. We identified numerous genotypic duplicates within and between collections and suggested preferred names to be used in the future. We found the BIH germplasm to have relatively few parent-offspring relationships, particularly among local cultivars, which might reflect the country’s history and patterns of apple cultivar introduction. A number of cultivars unique to BIH and a weakly defined genetic group were identified via STRUCTURE analysis, representing interesting targets for future research and preservation efforts.

Melanoma is the most severe type of skin cancer and among the most malignant neoplasms in humans. With the growing incidence of melanoma, increased numbers of therapeutic options, and the potential to target specific proteins, understanding the basic mechanisms underlying the disease’s progression and resistance to treatment has never been more important. LOXL3, SNAI1, and NES are key factors in melanoma genesis, regulating tumor growth, metastasis, and cellular differentiation. In our study, we explored the potential role of LOXL3, SNAI1, and NES in melanoma progression and metastasis among patients with dysplastic nevi, melanoma in situ, and BRAF+ and BRAF− metastatic melanoma, using immunofluorescence and qPCR analysis. Our results reveal a significant increase in LOXL3 expression and the highest NES expression in BRAF+ melanoma compared to BRAF−, dysplastic nevi, and melanoma in situ. As for SNAI1, the highest expression was observed in the metastatic melanoma group, without significant differences among groups. We found co-expression of LOXL3 and SNAI1 in the perinuclear area of all investigated subgroups and NES and SNAI1 co-expression in melanoma cells. These findings suggest a codependence or collaboration between these markers in melanoma EMT, suggesting new potential therapeutic interventions to block the EMT cascade that could significantly affect survival in many melanoma patients.

The pyrimidine heterocycle plays an important role in anticancer research. In particular, the pyrimidine derivative families of uracil show promise as structural scaffolds relevant to cervical cancer. This group of chemicals lacks data-driven machine learning quantitative structure-activity relationships (QSARs) that allow for generalization and predictive capabilities in the search for new active compounds. To achieve this, a dataset of pyrimidine and uracil compounds from ChEMBL were collected and curated. A workflow was developed for data-driven machine learning QSAR using an intuitive dataset design and forwards selection of molecular descriptors. The model was thoroughly externally validated against available data. Blind validation was also performed by synthesis and antiproliferative evaluation of new synthesized uracil-based and pyrimidine derivatives. The most active compound among new synthesized derivatives, 2,4,5-trisubstituted pyrimidine was predicted with the QSAR model with differences of 0.02 compared to experimentally tested activity.

With the dynamic nature of modern software development and operations environments and the increasing complexity of cloud-based software systems, traditional monitoring practices are often insufficient to timely identify and handle unexpected operational failures. To address these challenges, this paper presents the findings from a quantitative industry survey focused on the application of Machine Learning (ML) to enhance software monitoring and alert management strategies. The survey targets industry professionals, aiming to understand the current challenges and future trends in ML-driven software monitoring. We analyze 25 responses from 11 different software companies to conclude if and how ML is being integrated into their monitoring systems. Key findings revealed a growing but still limited reliance on ML to intelligently filter raw monitoring data, prioritize issues, and respond to system alerts, thereby improving operational efficiency and system reliability. The paper also discusses the barriers to adopting ML-based solutions and provides insights into the future direction of software monitoring.

While control barrier functions are employed in addressing safety, control synthesis methods based on them generally rely on accurate system dynamics. This is a critical limitation, since the dynamics of complex systems are often not fully known. Supervised machine learning techniques hold great promise for alleviating this weakness by inferring models from data. We propose a novel control barrier function-based framework for safe control through event-triggered learning, which switches between prioritizing control performance and improving model accuracy based on the uncertainty of the learned model. By updating a Gaussian process model with training points gathered online, the approach guarantees the feasibility of control barrier function conditions with high probability, such that safety can be ensured in a data-efficient manner. Furthermore, we establish the absence of Zeno behavior in the triggering scheme, and extend the algorithm to sampled-data realizations by accounting for inter-sampling effects. The effectiveness of the proposed approach and theory is demonstrated in simulations.

Organic materials have found widespread applications but require doping to overcome their intrinsically low carrier concentration. Doping injects free carriers into the polymer, moving the position of the Fermi level, and creates coulombic traps, changing the shape of the electronic density of states (DOS). We develop equations to explicitly map the DOS parameters to the Seebeck vs conductivity relationship. At low carrier concentrations, this relationship is a universal slope -kB/q\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$-{k}_{B}/q$$\end{document}, while at higher carrier concentrations, the slope becomes dependent on the shape of the DOS. We conclude that, at high doping, a heavy-tailed DOS leads to higher thermoelectric power factors.

The Rasch‐Built Pompe‐Specific Activity (R‐PAct) scale is a patient‐reported outcome measure specifically designed to quantify the effects of Pompe disease on daily life activities, developed for use in Dutch‐ and English‐speaking countries. This study aimed to validate the R‐PAct for use in other countries.

This paper is the first to analyse the role of women authors in fostering justice-relevant topics in climate adaptation research. As representation, citation and payment patterns remain gender-biased across scientific disciplines, we explore the case of climate science, particularly adaptation, as its most human-oriented facet. In climate research and policy, there has been a recent surge of interest in climate justice topics: mentions of justice have increased almost tenfold in Intergovernmental Panel on Climate Change Working Group 2 reports between the latest assessment cycles (AR5 and AR6). We conduct a systematic examination of the topic space in the adaptation policy scholarship. As it is a vast and rapidly growing field, we use topic modelling, an unsupervised machine learning method, to identify the literature on climate justice and related fields, as well as to examine the relationship between topic prevalence and the gender of the authors. We find climate change adaptation policy research to be male dominated, with women holding 38.8% of first and 28.8% of last authorships. However, we observe topic-specific variability, whereby the share of female authors is higher among publications on justice-relevant topics. Female authorship is highly linked to topics such as Community, Local Knowledge, and Governance, but less to Food Security and Climate Finance. Our findings corroborate the evidence that female authors play a significant role in advancing the research and dialogue on the relationship between climate change and areas that have meaningful impact on lives of women and other marginalised groups.

While control barrier functions (CBFs) are employed in addressing safety, control synthesis methods based on them generally rely on accurate system dynamics. This is a critical limitation, since the dynamics of complex systems are often not fully known. Supervised machine learning techniques hold great promise for alleviating this weakness by inferring models from data. We propose a novel \revision{approach for safe event-triggered learning of Gaussian process models in CBF-based continuous-time control for unknown control-affine systems. By applying a finite excitation at triggering times, our approach ensures a sufficient information gain to maintain the feasibility of the CBF-based safety condition with high probability. Our approach probabilistically guarantees safety based on a suitable GP prior and rules out} Zeno behavior in the triggering scheme. The effectiveness of the proposed approach and theory is demonstrated in simulations.

This study investigates the use of neural network and their ability to predict disease progression based on clinical data and biomarkers. Using deep neural networks, a model was developed that efficiently analyzes the complex relationship between various factors and predict the probability of disease. The model was validated using retrospective analysis which indicated a good predictive ability that could be further utilized in better diagnostics and personalized treatment methods. More importantly, reserch detected specific pattern in the data, which enabled a more accurate prediction of disease at different stages. The study tried to improve a model by fine-tuned neural networks and tested other frameworks to gain the highets precision. This research also provides a basic for future work in directing the development of personalized therapeutic approaches based on individual patient characteristics.

Abstract The aim of this study was to evaluate the phytotoxic, genotoxic, cytotoxic and antimicrobial effects of the Mentha arvensis L. essential oil (EO). The biological activity of M. arvensis EO depended on the analyzed variable and the tested oil concentration. Higher concentrations of EO (20 and 30 µg mL−1) showed a moderate inhibitory effect on the germination and growth of seedlings of tested weed species (Bellis perennis, Cyanus segetum, Daucus carota, Leucanthemum vulgare, Matricaria chamomilla, Nepeta cataria, Taraxacum officinale, Trifolium repens and Verbena × hybrida). The results obtained also indicate that the EO of M. arvensis has some genotoxic, cytotoxic and proliferative potential in both plant and human in vitro systems. Similar results were obtained for antimicrobial activity against eight bacteria, including multidrug-resistant (MDR) strains [Bacillus subtilis, Enterococcus faecalis, Staphylococcus aureus, methicillin-resistant S. aureus (MRSA), Escherichia coli, extended-spectrum beta-lactamase-producing (ESBL) E. coli, Pseudomonas aeruginosa and Salmonella enterica subsp. enterica serovar Enteritidis], with the effect on multidrug-resistant bacterial strains. Research indicates that the EO of M. arvensis shows phytotoxic, genotoxic, cytotoxic and antimicrobial effects, as well as its potential application as a herbicide and against various human diseases.

Using the strong-field-approximation theory beyond the dipole approximation we investigate above-threshold ionization induced by the monochromatic and bichromatic laser fields. Particular emphasis is on the approach based on the saddle-point method and the quantum-orbit theory which provides an intuitive picture of the underlying process. In particular, we investigate how the solutions of the saddle-point equations and the corresponding quantum orbits and velocities are affected by the nondipole effects. The photoelectron trajectories are two dimensional for linearly polarized field and three dimensional for two-component tailored fields, and the electron motion in the propagation direction appears due to the nondipole corrections. We show that the influence of these corrections is not the same for all contributions of different saddle-point solutions. For a linearly polarized driving field, we focus our attention only on the rescattered electrons. On the other hand, for the tailored driving field, exemplified by the ω–2ω orthogonally polarized two-color field, which is of the current interest in the strong-field community, we devote our attention to both the direct and the rescattered electrons. In this case, we quantitatively investigate the shift which appears in the photoelectron momentum distribution due to the nondipole effects and explain how these corrections affect the quantum orbits and velocities which correspond to the saddle-point solutions. Published by the American Physical Society 2024

Data-driven models that act as surrogates for computationally costly 3D topology optimization techniques are very popular because they help alleviate multiple time-consuming 3D finite element analyses during optimization. In this study, one such 3D CNN-based surrogate model for the topology optimization of Schoen’s gyroid triply periodic minimal surface unit cell is investigated. Gyroid-like unit cells are designed using a voxel algorithm and homogenization-based topology optimization codes in MATLAB. A few such optimization data are used as input–output for supervised learning of the topology-optimization process via the 3D CNN model in Python code. These models could then be used to instantaneously predict the optimized unit cell geometry for any topology parameters. The high accuracy of the model was demonstrated by a low mean square error metric and a high Dice coefficient metric. The model has the major disadvantage of running numerous costly topology optimization runs but has the advantages that the trained model can be reused for different cases of TO and that the methodology of the accelerated design of 3D metamaterials can be extended for designing any complex, computationally costly problems of metamaterials with multi-objective properties or multiscale applications. The main purpose of this paper is to provide the complete associated MATLAB and PYTHON codes for optimizing the topology of any cellular structure and predicting new topologies using deep learning for educational purposes.

The emergence of bacteria with antibiotic resistance and multiple resistance is characteristic of animal and human pathogens. It is wide known that bee products, which have been used in alternative medicine since ancient times, have antimicrobial potential. Application of bee products for therapeutic purposes is defined as apitherapy. The study aimed to evaluate the antimicrobial activity of commercial chestnut honey, pollen and propolis produced in western Bosnia and Herzegovina (Sanski Most) individually and in five combinations (apimixtures). The antimicrobial properties of samples were investigated using the agar well diffusion method against three Gram-positive bacteria (Bacillus subtilis subsp. spizizenii ATCC 6633, Methicillin-resistant Staphylococcus aureus ATCC 33591, Enterococcus faecalis ATCC 29212); three Gram-negative bacteria (ESBL producing Escherichia coli ATCC 35218, Salmonella enterica subsp. enterica serovar Enteritidis ATCC 13076, Pseudomonas aeruginosa ATCC 9027) and one fungal species (Candida albicans ATCC 10231). Pure bee pollen inhibited the growth of only Gram-negative bacteria, concentrated chestnut honey was active against all Gram-negative and Gram-positivebacteria, while 20% propolis extract and apimixtures A2 (80% honey and 20% propolis) and A3 (60% honey, 20% pollen and 20% propolis extract) inhibited the growth of all tested microorganisms. Chestnut honey andthree apimixtures (A1, A2 and A3) showed the highest antibacterial action against all tested Gram-negative bacteria and MRSA compared to other investigated samples. In this study, examined honeybee products from Bosnia and Herzegovina and their mixtures had significant activity against tested bacteria, including strains with proven resistance to conventional antibiotics, MRSA and ESBL producing E. coli.