BACKGROUND Tooth wear is a non-pathological loss of hard tissues on the incisal and occlusal tooth surface. In archaeology, the loss of dental tissue through attrition is associated with living opportunities and habits, availability, characteristics and methods of food preparation. In forensics, tooth wear is used to estimate the dental age on cadavers. MATERIAL AND METHODS For this study, we used an archaeological sample from two sample collections. In this study, tooth wear was compared in archaeological samples of well-preserved maxilla and mandible specimens (n=392) from Croatian coastal and continental populations from Late Antiquity (LA) and the Early Middle Ages (EMA). The computer system VistaMetrix 1.38 was used to analyse the abrasion and attrition of hard dental tissues. The Shapiro-Wilk and chi-square tests were performed for categorical data to test the difference between two historical periods and two geographical locations, while the Kruskal-Wallis test was performed for continuous data. RESULTS There was a statistically significant difference in the proportion of tooth wear in total teeth area (P < 0.001) when comparing continental and coastal Croatia in LA and coastal Croatia between LA and EMA (P = 0.006 and P < 0.001, respectively). Samples from coastal Croatia from the LA period had the lowest percentage of tooth wear with a median of 8.35%, while samples from coastal Croatia from the EMA had the highest percentage of tooth wear with a median of 18.26%. Our results generally show greater tooth wear in the EMA period in male subjects. CONCLUSION The results of the tooth wear research obtained with the Vista Metrix software can contribute to the study of life circumstances and changes that the analysed population has experienced in its historical development.

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.

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.

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.

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.

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.

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.

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.

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.

Using socially assistive robots (SARs) as specialized companions for those living with depression to manage symptoms provides a unique opportunity for exploration of robotic systems as comfort objects. Moreover, the robotic components allow for specialized behavioral responses to particular stimuli, as preferred by the user. We have conducted semi-structured interviews with 10 participants about the zoomorphic robot’s Therabot™ desired behaviors and focus groups with five additional participants regarding the preferred sensors within the Therabot™ system. In this paper, using the data from interviews and focus groups, we explore SAR input and output for depression management. While participants overall expected the robot to respond in much similar ways as a well-trained service animal, they expressed interest in the robot understanding unique information about the environment and the user, such as when the user might need interaction.

This paper introduces a "lifecycle perspective" on social robot design and human-robot interaction, and explores the practices of maintenance, repair, and letting go of social robots. Drawing on interviews with robot owners and representatives of robot development and repair companies, we argue that these previously disregarded aspects of everyday use provide a context for negotiating the social value and meaning of interactions with robots. We discuss owner concerns about robot obsolescence, as well as company support for long term human-robot interaction through repair, reuse, and giving owners closure in letting go of robots they can no longer use. Our work expands the purview of HRI study and design beyond the common focus on initial design and adoption and to perceptions and practices that can foster more enduring relationships with social robots, support sustainability in robot design, and address owners’ emotional attachment to robots.

Under UNICEF’s Policy guidance on AI for children, child-centered AI should always ‘ensure inclusion of and for children.’ To spotlight youth visions for robots, we led co-design workshops with children between 5-14 years old. Youth designs were expressive, customized, relatable, and approachable. Based on 54 drawings and descriptions of the social robot Haru, we suggest that future child-centered robots should 1) be expressive across verbal and non-verbal channels of communication, 2) allow for customization to give children more agency when interacting with the robot, 3) adapt to children’s style and hobbies to make them feel seen, and 4) aesthetically keep proportions of robot faces consistent and cartoon-like to make robots more approachable.

The sources of a person’s ikigai—their sense of meaning and purpose in life—often change as they age. Reflecting on past and new sources of ikigai may help people renew their sense of meaning as their life circumstances shift. Building on insights from an initial Wizard-of-Oz robot prototype [1], we describe the design of an autonomous robot that uses a semi-structured conversation format to help older adults reflect on what gives their life meaning and purpose. The robot uses both pre-determined (scripted) and Large Language Model (LLM) generated questions to personalize conversations with older adults around themes of social interaction, planning, accomplishments, goal setting, and the recent past. We evaluated the autonomous robot with 19 older adult participants in a lab setting and at two eldercare facilities. Analysis of the older adults’ conversations with the robot and their responses to an evaluative survey allowed us to identify several design considerations for an autonomous robot that can support ikigai reflection. Interweaving simple yet detailed predetermined questions with LLM-generated follow-up questions yielded enjoyable, in-depth conversations with older adults. We also recognized the need for the robot to be able to offer relevant suggestions when participants cannot recall events and people they find meaningful. These findings aim to further refine the design of an interactive robot that can support users in their exploration of life’s purpose.