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Phospholipase A2 (PLA2) is a prevalent molecule in the honeybee venom. Its importance is reflected by the number of scientists focused on studying it from various points of view. This review summarises a significant amount of data concerning this fascinating substance. Firstly, the origin and occurrence of PLA2, with similarities and differences among species or populations of bees are highlighted. Next, its synthesis, post-translational processing and structural features are described, followed by the PLA2 availability. In a larger section, the multiple effects of honeybee venom PLA2 are detailed, starting with the main ability as an enzyme to interact with biological membranes and to hydrolyse the sn-2 ester bond in 1,2-diacyl-sn-3-phosphoglycerides; the docking process, the substrate binding and the catalytic steps are analysed too. Then, the pro-/anti-inflammatory effect and allergenic property, the anticoagulant effect and the involvement of PLA2 in apoptosis are revised. Selected antiviral, antibiotic and antitumoral effects of PLA2, as well as its use in immunotherapy are mentioned as beneficial applications. Additionally, the mechanisms of toxicity of PLA2 are presented in detail. Finally, a number of anti-PLA2 compounds are enumerated. In each section, the features of the honeybee venom molecule are discussed in relation to PLA2s from other species.

We introduce an improved version of Random Search (RS), used here for hyperparameter optimization of machine learning algorithms. Unlike the standard RS, which generates for each trial new values for all hyperparameters, we generate new values for each hyperparameter with a probability of change. The intuition behind our approach is that a value that already triggered a good result is a good candidate for the next step, and should be tested in new combinations of hyperparameter values. Within the same computational budget, our method yields better results than the standard RS. Our theoretical results prove this statement. We test our method on a variation of one of the most commonly used objective function for this class of problems (the Grievank function) and for the hyperparameter optimization of a deep learning CNN architecture. Our results can be generalized to any optimization problem dened on a discrete domain.

Nearly all model algorithms used in machine learning use two different sets of parameters: the training parameters and the meta-parameters (hyperparameters). While the training parameters are learned during the training phase, the values of the hyperparameters have to be specified before learning starts. For a given dataset, we would like to find the optimal combination of hyperparameter values, in a reasonable amount of time. This is a challenging task because of its computational complexity. In previous work [11], we introduced the Weighted Random Search (WRS) method, a combination of Random Search (RS) and probabilistic greedy heuristic. In the current paper, we compare the WRS method with several state-of-the art hyperparameter optimization methods with respect to Convolutional Neural Network (CNN) hyperparameter optimization. The criterion is the classification accuracy achieved within the same number of tested combinations of hyperparameter values. According to our experiments, the WRS algorithm outperforms the other methods.

Industry 4.0, Smart Manufacturing, Factories of the Future all describe aspects of the heralding era of digitalization of manufacturing aiming to interconnect every step of the manufacturing process and seamlessly integrate the physical and digital world. In Factories of the Future a central computer organizes the intelligent networking of all subsystems, suppliers and customers into one system. All relevant requirements concerning manufacturing and product are confirmed at design time, while execution takes place autonomously as ICT and automation are integrated. The main challenge is represented by educational system, how prepared is to provide students, future employees, the digital competences necessary for the Factories of the Future. What are the structural and curricular measures Higher Education Institutions need to take in order to align engineering education, especially in the design of all constituents of Factories of the Future, with the need of competences in new manufacturing era? A quantitative analysis of existing study programs aims understanding the status quo of Master programs in engineering education and, deriving from existing policy documents potential requirements for competences design of Factory of the Future employees.

NRC publication: Yes

NRC publication: Yes

This article aims at identification of the main trends in scientific literature characterising urban smart mobility, on the basis of bibliometric analysis of articles published in the ISI Web of Science and Scopus databases. The study period was set from 2000 to 2017. Authors used a basic technique of the bibliometric analysis of the scientific literature characterising urban smart mobility with the support of the VOSviewer software. The analysis included the number of publications, citation analysis, research area analysis and the most frequent keywords. The analysis led to taking notice of current research trends dealing with the urban smart mobility. The core of the paper is a theoretical framework of research trends, which was developed through a review of scientific literature. The result of this paper is a map showing the existing relationships between key terms, research areas characterising publications dealing with the urban smart mobility and intelligent transport system (ITS). “Smart city” is probably the most “in vogue”, debated and analysed concept among researchers and administrative/ governmental representatives from all over the world. This multidimensional concept is mainly based on smart technology structured around few major components: smart mobility, smart environment, smart governance, smart living, and everything that targets the people’s wellbeing. This work focuses on a hot topic – mobility because of its significant impact on the environment by pollution as well as living by requiring intelligent transport systems.

Since the World Health Organization declared COVID-19 a pandemic in 2020, the global community has faced ongoing challenges in controlling and mitigating the transmission of the SARS-CoV-2 virus, as well as its evolving subvariants and recombinants. A significant challenge during the pandemic has not only been the accurate detection of positive cases but also the efficient prediction of risks associated with complications and patient survival probabilities. These tasks entail considerable clinical resource allocation and attention. In this study, we introduce COVID-Net Biochem, a versatile and explainable framework for constructing machine learning models. We apply this framework to predict COVID-19 patient survival and the likelihood of developing Acute Kidney Injury during hospitalization, utilizing clinical and biochemical data in a transparent, systematic approach. The proposed approach advances machine learning model design by seamlessly integrating domain expertise with explainability tools, enabling model decisions to be based on key biomarkers. This fosters a more transparent and interpretable decision-making process made by machines specifically for medical applications. More specifically, the framework comprises two phases: In the first phase, referred to as the “clinician-guided design” phase, the dataset is preprocessed using explainable AI and domain expert input. To better demonstrate this phase, we prepared a benchmark dataset of carefully curated clinical and biochemical markers based on clinician assessments for survival and kidney injury prediction in COVID-19 patients. This dataset was selected from a patient cohort of 1366 individuals at Stony Brook University. Moreover, we designed and trained a diverse collection of machine learning models, encompassing gradient-based boosting tree architectures and deep transformer architectures, specifically for survival and kidney injury prediction based on the selected markers. In the second phase, called the “explainability-driven design refinement” phase, the proposed framework employs explainability methods to not only gain a deeper understanding of each model’s decision-making process but also to identify the overall impact of individual clinical and biochemical markers for bias identification. In this context, we used the models constructed in the previous phase for the prediction task and analyzed the explainability outcomes alongside a clinician with over 8 years of experience to gain a deeper understanding of the clinical validity of the decisions made. The explainability-driven insights obtained, in conjunction with the associated clinical feedback, are then utilized to guide and refine the training policies and architectural design iteratively. This process aims to enhance not only the prediction performance but also the clinical validity and trustworthiness of the final machine learning models. Employing the proposed explainability-driven framework, we attained 93.55% accuracy in survival prediction and 88.05% accuracy in predicting kidney injury complications. The models have been made available through an open-source platform. Although not a production-ready solution, this study aims to serve as a catalyst for clinical scientists, machine learning researchers, and citizen scientists to develop innovative and trustworthy clinical decision support solutions, ultimately assisting clinicians worldwide in managing pandemic outcomes.

Hepatocellular carcinoma is the most common primary malignancy of the liver, with hepatocellular differentiation. It is ranked sixth among the most common cancers worldwide and is the third leading cause of cancer-related deaths. The most important etiological factors discussed here are viral infection (HBV, HCV), exposure to aflatoxin B1, metabolic syndrome, and obesity (as an independent factor). Directly or indirectly, they induce chromosomal aberrations, mutations, and epigenetic changes in specific genes involved in intracellular signaling pathways, responsible for synthesis of growth factors, cell proliferation, differentiation, survival, the metastasis process (including the epithelial–mesenchymal transition and the expression of adhesion molecules), and angiogenesis. All these disrupted molecular mechanisms contribute to hepatocarcinogenesis. Furthermore, equally important is the interaction between tumor cells and the components of the tumor microenvironment: inflammatory cells and macrophages—predominantly with a pro-tumoral role—hepatic stellate cells, tumor-associated fibroblasts, cancer stem cells, extracellular vesicles, and the extracellular matrix. In this paper, we reviewed the molecular biology of hepatocellular carcinoma and the intricate mechanisms involved in hepatocarcinogenesis, and we highlighted how certain signaling pathways can be pharmacologically influenced at various levels with specific molecules. Additionally, we mentioned several examples of recent clinical trials and briefly described the current treatment protocol according to the NCCN guidelines.

Bee venom (BV) molecules, including melittin (Mlt), are known to modify the permeability of membranes. This paper assessed red blood cell (RBC) shape (by phase contrast microscopy) in relation to some of the parameters (haematology data) and calculated the RBC membranes’ water diffusional permeability (Pd) with 1H-NMR spectroscopy. Rats were injected for 30 days with either small daily doses of BV (VST) or Mlt (MST) or with high single doses of BV (VSLT) or Mlt (MSLT). The RBCs displayed aberrant shapes, all of the analysed parameters significantly changed, and the values of Pd were higher (and increased with temperature) in all of the treated groups compared to the control group. The RBCs in the venom-treated groups had the highest mean values (expressed in cm × s−1 × 103) of Pd at 37 °C—8.95 in the VSLT group and 8.69 in the VST group—which were followed by the MST and MSLT groups and the control group. Our results demonstrated the ability of Mlt to retain the ability to interact with the RBC membrane in vivo and proved that Mlt is the most important BV molecule involved in this process.