Introduction: Miscarriages affect 50-70% of all gestations and 15-20% of clinically recognized pregnancies. Recurrent pregnancy loss (RPL) occurs in 1-5% of clinical pregnancies and has an enormous demographic impact. However, the etiologies and molecular pathways of RPL are scarcely understood, and therefore, reliable diagnostic and preventive methods are not yet available. Here, we aimed to discover novel biomarkers for RPL using next-generation proteomics technology to help develop early and effective diagnostic tools. Methods: First-trimester blood samples were collected from women with RPL (n=11) and controls with elective termination of pregnancy (n=11) between 6–13 weeks of gestation. After immunodepleting 14 highly abundant proteins, plasma samples were reduced, alkylated, and trypsin digested. For the separation of peptides, nano-flow reversed-phase chromatography was applied, and then mass spectrometric analysis was performed. Differentially abundant (DA) proteins were identified using strict criteria and analyzed by protein network and Gene Ontology (GO) enrichment analyses, and two biomarker candidates (CGB and PAPPA) were validated by immunoassay. Biomarker predictive properties were demonstrated using Receiver Operating Characteristic (ROC) curves. Assessments were performed for all cases and then for two gestational age groups, before and after the start of placental circulation [“early RPL”: gestational weeks (GW) 6–9, “late RPL”: GW 9–13]. Results: Altogether, 651 proteins were identified and quantified across all samples. When comparing “early control” and “late control” samples, 60 proteins [11 predominantly placenta-expressed (PPE)] were DA. When analyzing all cases, 50 DA proteins were found in RPL (top 3 down: PZP, PSG9, CGB; top 3 up: C4BPA, HBA, HBB), among which 11 PPE proteins were found, all downregulated. Enriched GO terms included ‘placental function’, ‘oxidative processes’, ‘immune function’, and ‘blood coagulation’ related biological processes. When cases were split into early and late RPL groups, 40 DA proteins were identified in early RPL (top 3 down: SHBG, CGB, CGA; top 3 up: C4BPA, SAMP, C4BPB) and 90 in late RPL (top 3 down: PZP, PAPPA, PSG9; top 3 up: THBS1, ECM1, HBB), among which only 15 were shared by both RPL groups. In early RPL, only ‘placental function’ and ‘immune function’ related biological processes were enriched, while in late RPL the top enriched GO terms included ‘placental function’, ‘oxidative processes’, ‘immune function’, ‘blood coagulation’, ‘angiogenesis’, ‘cell migration’, and ‘blood circulation’ related biological processes. Among GO terms, only ‘placental function’ related biological processes were enriched when early- and late RPL DA proteins were analyzed together. Furthermore, the areas under the ROC curves were >0.9 for two protein candidates in all RPL, for five proteins in early RPL, and for ten proteins in late RPL. Among these candidates, CGB and PAPPA were validated by immunoassay which showed a good correlation with MS data (RCGB=0.795 and RPAPPA=0.965). Conclusion: We discovered distinct as well as shared molecular pathways associated with RPL pathogenesis before and after the start of placental circulation and identified novel biomarkers for these pathways which have outstanding discriminative properties. Our results may facilitate a better understanding of the molecular pathways of RPL. However, larger clinical studies are needed to investigate whether the identified biomarkers also have predictive power for RPL before pregnancies fail and to test drugs for the modulation of the identified disease pathways and the prevention of RPL. Our findings highlight the importance of the maternal immune system in maintaining successful pregnancy and suggest that targeting immune pathways may offer novel therapeutic approaches for RPL.

In this paper, we explore the utility of classical neural network-based approaches, originally designed for processing 2D images, in semantic segmentation and object recognition tasks within the context of 3D point cloud images generated from handheld video recordings. Our investigation centers around the use of a custom-created, small-sized training dataset, consisting of 108 RGB images of humans and cobots in diverse industrial settings. This dataset allows us to demonstrate that flexible segmentation and recognition applications can be built even with a restricted dataset developed using widely available low-cost tools and modern convolutional neural net architectures. Downstream benefits of the approach include the ability to detect humans and human gestures, as well as to rapidly prototype digital twins in Industry 5.0 environments.

The first occurrence of the widely distributed predatory mite Macrocheles robustulus (Berlese, 1904) on the cetoniin beetle pet species, Pachnoda marginata peregrina Kolbe is presented. A short description and new illustration are provided with a note on macrochelid mites associated with cetoniin beetles.

The equivalent T-stub technique is widely utilized as a design solution for steel bolted connections, which are otherwise complex to analyze. However, traditional standard-based methods often lack the precision required for accurate analysis, particularly when accounting for dynamic effects, such as those caused by earthquakes, leading to designs that may not be sustainable. This research addresses this issue by introducing a framework aimed at optimizing the bolt layout in a selected T-stub connection to maximize structural performance under cyclic loading, thereby enhancing sustainability. The finite element method (FEM) was employed to account for nonlinear characteristics, including the elastic-plastic behavior of steel, large deformations, and contact nonlinearities, ensuring precise analysis. The developed T-stub model was validated against experimental tests to reflect real-world behavior, utilizing ABAQUS finite element software. Optimization was conducted using a genetic algorithm (GA) implemented in the PYTHON programming language, linked to the simulation process. The results demonstrate the effectiveness of the proposed framework in significantly enhancing the structural performance of the steel T-stub connection under cyclic loading conditions without requiring additional material, thereby contributing to a more sustainable design.

Automated Driving functions have widely spread in last years, not only in research, but also in the automotive industry. Many car manufacturers and their suppliers have funded different software systems to fulfill the driving requirements. By the growing number of innovations, the rapidly changing technology and newer players in the automotive industry requires academy and industry to collaborate on their automated software developments. To do so, a common research and development framework is needed which inherently provides smooth transition of new solutions from one world to the other. Even though there are many open source research framework for rapid prototyping of automated driving functions, these solutions are often concentrated on low speed maneuvering, especially for the higher automation levels, such as robo-taxis. However, major automotive companies require developments of up to SAE (Society of Automotive Engineers) level 2 and 3. Therefore, a new prototypical, open source software architecture¹ is proposed, that uses well-known tools and technology as basis, such as Robot Operating System or Autoware Universe, but also being closely in compliance with industrial architecture aspects. It is proven that the proposed architecture, along with its abstraction layers and interfaces provide modularity, and interchangeability of basic components, while the hardware and vehicle dependencies are well separated from the application software layer. Via few exemplary functions the usability of the system is demonstrated, and fellow researchers are highly encouraged to advise on the generalization of the system.¹https://github.com/jkk-research/CooperativeResearchPlatform/tree/release

High ambient temperature represents an increasingly frequent challenge for animal farming, especially for those animal species more susceptible to heat stress (HS), like the rabbit. The present research studied the impact of different ambient temperatures (T: 20 °C—Control vs. 28 °C—High) on the meat quality of two rabbit lines (L: Fat line, Lean line) obtained after 5 generations of divergent selection for total body fat content. After slaughter, the ground meat of 60 carcases (15 rabbits/treatment) was used for physicochemical and sensory quality evaluation. Overall, high T affected hind leg weight (p < 0.001), pHu (p = 0.001), and oxidative status (p = 0.004) during a shelf-life trial. High T increased meat haem-iron (p < 0.001), decreased lipids (p < 0.001), MUFA (p < 0.001), and PUFA classes (p < 0.001), and consequently, increased water content (p < 0.001). Regarding L effect, Fat line was richer in lipids (p < 0.001) and ash (p = 0.008), but less rich in water (p < 0.001) than Lean line. The content of all fatty acid (FA) classes was therefore significantly higher in Fat line meat (p < 0.001). It can be concluded that the two genotypes differed for proximate composition, haem-iron, FA, and amino acid profiles of carcase meat. High T increased meat pHu, water, haem-iron, and reduced polyunsaturated FA (PUFA) amount. At high T the meat of the Fat line showed higher TBARS, whereas Lean line had higher lysine content. Sensory analysis revealed that high T improved tenderness and extinguished onion off-flavour.

The paper analyses the practice of the constitutional courts of Hungary, Serbia and Croatia, in terms of the constitutionality and legality of the normative responses to the COVID-19 pandemic in the countries examined. The goal is to critically present the arguments along which the constitutional courts ensured (or attempted to achieve) the balance between the protection of fundamental rights and the preservation of the public interest and public health in their decisions related to the COVID-19 pandemic; and to deduce whether any similarities can be discovered in the reasoning of the courts or they have adopted a completely different approach from each other. According to the results of the legislative research, regional experience of the examined neighbouring countries with similar legal and political traditions, constitutional court structures, and political leadership styles shows that even in circumstances of a global, uniform health crisis, distinct national reactions might be expected. However, on the other side, the case law research gave a completely different conclusion, supporting the highly similar reasoning of the constitutional courts that almost without exception have given priority to public interest in combating the epidemic over fundamental rights.

High-resolution, dynamic geospatial data support sustainable infrastructure, optimize urban services, and improve the quality of life of residents. Airborne Laser Scanning is an increasingly popular remote sensing technology that can be used to collect very large datasets of 3D point clouds over extensive areas, including forests, river basins, coastal wetlands, and mountainous regions. These datasets facilitate the analysis of vegetation structure, biomass estimation, hydrological modeling, and land cover detection or change monitoring. However, Airborne Laser Scanning-derived point clouds are typically limited to low density and spatial resolution, which preclude meaningful analysis for fine-scale ecological and environmental modeling. Point cloud upsampling is a permissible way to augment the spatial robustness of Airborne Laser Scanning point cloud data, and does so without adding a logistical burden of the data acquisition in the field, or the need to resurvey at high costs and time. Upsampling is synthetic in nature, achieving increased data point count, but maintaining dimensional integrity for continuity of surfaces and geometric fidelity, which is essential in methodologies that intervene for derived products such as digital terrain models, canopy height models, and vegetation metrics. This manuscript examines using point cloud upsampling as part of environmental monitoring. It reviews the upsampling algorithms that have been developed to date, synthesizes existing methods, and considers their relevance to the state of practice in forestry, watershed management, and conservation planning. The work considers and focuses on methodological bases for robustness and dimensionality, and although considerably nuanced, the methodological efficacy is subtended and suggests how enhanced points improve outcomes for ecological models and the information provided supports resource management decisions related to resource and sustainability decisions.. Ultimately and conclusively, the work establishes the understanding of point cloud enhancement for its visualization, but also its potential as an emergent action that contributes to construction and promotes a sustainability intention in environmental science and policy. This article appears as a mini-review. This writing aims to synthesise existing knowledge and conceptual strategies, rather than a novel outcome of an experiment. It is written to give an overview of existing methods and structured conceptual frameworks for employing point cloud upsampling techniques in the environmental monitoring and sustainability context. The review reiterates the conceptual soundness of point cloud upsampling in the workflow of environmental monitoring. The proposed framework reinforces the benefits of greater data richness and decision-making based on sustainability, without assuming new costs for data generation.

Fuzzy signatures have demonstrated effectiveness in various knowledge-representation domains, including medical decision-making systems and complex decision-making tasks across numerous fields. Even though the advancements in the field of fuzzy signatures have been substantial, the complete potential for developing a comprehensive graph-theoretical description format for this domain still needs to be fully realized. This paper introduces a novel hypergraph-based method for modeling fuzzy signatures, which offers a structured approach to their representation but also showcases the potential synergy between fuzzy signatures and hypergraphs. The proposed method is designed to improve fuzzy information representation and streamline the aggregation-based decision-making process. Future research is anticipated to extend the applicability of this method to control systems and robotics. Furthermore, the hypergraph-based model opens new avenues for the algebraic analysis of fuzzy signatures through tensor-based representations.