Leveraging Productivity Analysis for Smallholders’ Sustainable Development: Dairy Efficiency in Central Madagascar’s Crop-Livestock Family Farms

Milk production in developing African countries is a viable path for smallholders’ sustainable development. Supporting interventions should be shaped by evidence from comprehensive, context-specific analyses. Using survey data, this study contributes to the development-oriented literature on dairy productivity in African smallholder systems by conducting the first stochastic frontier analysis in the Malagasy context. Focusing on milk producers in central Madagascar’s crop-livestock family farms, a stochastic frontier production function with inefficiency effects is developed. The fitted frontier comprises the number of cows, annual purchased feed expenditure, farmer’s labor, and total household assets owned. Distance from the frontier is explained by the use of improved breeds, integration in the regional milk zone, farmer years of experience, the presence of off-farm income, and the number of oxen owned. Technical efficiency ranged from 4.6% to 90.8% around a mean of 55.5%. Results revealed how, in this context, cows are embedded in diversified family farming systems where resources are allocated across production activities and household needs. The study’s multidisciplinary stochastic frontier analysis provides a more complete picture to guide research and policy for smallholders’ sustainable rural development.

A Multiplex Flow Cytometric Approach to Define Molecularly Distinct Extracellular Vesicle Subsets

Extracellular vesicles (EVs) are molecularly very heterogeneous, and their characterization at the single-particle level is technically challenging. Existing approaches, such as nanoparticle tracking analysis, fluorescence microscopy, and nano-flow cytometry, provide important insights but often lack the flexibility to detect multiple molecular markers simultaneously. Here, we describe an optimized workflow for multiparametric EV phenotyping using a spectral flow cytometry instrument with enhanced small particle detection capacity. EVs were isolated from murine melanoma and melanocyte cell lines via size-exclusion chromatography and labeled with a fluorogenic membrane probe that enables robust, single EV detection. In this study, we systematically optimized staining conditions, EV concentrations, and fluorophore combinations for a 5-color antibody panel on single EVs. We show that single-particle flow cytometry can reliably detect and resolve multiple EV surface markers simultaneously. Data analysis by unsupervised clustering further enabled unbiased identification of distinct EV subsets, providing a practical approach for EV phenotyping in both research and clinical contexts.

Extracellular Vesicles from Oral Squamous Cell Carcinoma Carry OGT/OGA with Possible Implications in Tumor O-GlcNAcylation

Oral squamous cell carcinoma (OSCC) is a malignant epithelial neoplasm characterized by high aggressiveness and limited options for early diagnosis. In recent years, extracellular vesicles (EVs) have gained attention as mediators of intercellular communication in cancer, contributing to tumor progression and remodeling of the microenvironment. O-GlcNAcylation, regulated by O-GlcNAc transferase (OGT) and O-GlcNAcase (OGA), participates in multiple tumor processes; however, its association with EVs in OSCC has not yet been explored. In this study, EVs were isolated from SCC-152, SCC-25, and HaCaT cell lines using differential centrifugation, and their identity was confirmed by detection of CD63 and TSG101 markers and by transmission electron microscopy (TEM). Immunocytochemistry revealed the nuclear and cytoplasmic localization of OGT and OGA in all analyzed cell lines. Importantly, both enzymes were detected in EVs cargo by Western blot analysis, with significant differences between tumor and non-tumor lines as determined by densitometric and fluorescence intensity analyses. Quantitative analysis indicated a higher relative signal for OGA compared with OGT across all cell lines (with an approximate ~1.5–2.2-fold difference depending on the cell line, p < 0.05), and cell line-derived samples showed a higher relative signal than non-tumoral HaCaT (corresponding to an approximate ~1.2–1.3-fold increase under the experimental conditions evaluated). All experiments were performed using three independent biological replicates (n = 3), and statistical significance was assessed using one-way or two-way ANOVA followed by Tukey’s post hoc test. These findings suggest that OSCC-derived EVs carry enzymatic components of the O-GlcNAcylation machinery as vesicular protein cargo, potentially influencing tumor microenvironment regulation and cancer progression. Overall, these results should be considered hypothesis-generating, opening new perspectives for their use as vesicular biomarkers.

Revisiting the Conservation Challenges of Wild Argali (Ovis ammon ammon L.) in the Altai Mountain-Steppe under Climate and Anthropogenic Pressures

The high-mountain steppes of South-eastern Altai are a valuable resource for pastoralism—almost the only possible type of economic activity in these places—and the conservation of near threatened species, such as the argali. Argali are the largest and most vulnerable wild sheep (Ovis ammon ammon L.), and are listed in the Red Data Book of the Russian Federation and on the IUCN Red List. The argali is also important in the food chain of another rare and protected species, the snow leopard. This paper presents the results of research into the productivity of argali’s high-mountain steppes habitation in various parts of the Sailugem Ridge, and assesses their pasture degradation. We predict how observed declining pasture productivity due to anthropogenic and climate pressure, as well as argali grazing, will threaten their survival. We propose special measures to reduce the impact of the argali population on the degradation of current pastures, while improving argali conservation in other areas of South-eastern Altai and adjacent territories where the species previously existed.

Antiviral Pharmaceuticals as Emerging Environmental Contaminants: Occurrence, Ecotoxicological Risks, and Photocatalytic Remediation Pathways

The widespread use of antiviral pharmaceuticals during and after the COVID-19 pandemic has raised growing concerns about their role as emerging environmental contaminants. These compounds, including favipiravir, remdesivir, molnupiravir, and oseltamivir carboxylate, are frequently detected in hospital effluents, municipal wastewater, and surface waters. Unlike many previous reviews that treat pharmaceuticals as a broad and undifferentiated class, this article focuses specifically on antiviral drugs as a distinct group of emerging contaminants and provides an integrated perspective that is still largely missing from the literature. As a review article, this work offers a critical and comprehensive synthesis that brings together environmental monitoring data, ecotoxicological and resistance-related risks, and advanced remediation strategies within a single framework. Particular emphasis is placed on recent advances in semiconductor-based photocatalytic degradation (TiO₂, ZnO, g-C₃N₄, and their hybrids) and on mechanistic insights supported by density functional theory (DFT) and machine-learning (ML) approaches, which are used to link molecular-level properties to degradation efficiency and pathway selectivity. By systematically combining occurrence patterns, risk assessment, and DFT/ML-informed photocatalysis—specifically for antiviral pharmaceuticals—this review is among the first to delineate design principles and knowledge gaps unique to this drug class. The article highlights critical research needs and outlines future directions toward reproducible, computationally guided, and scalable treatment technologies for antiviral pollutants.

A Review of Bolted Connections for the Assembly of Floating Offshore Wind Turbine Foundations

Bolted connections are being considered as an assembly method for the foundations of floating offshore wind turbines. A clear benefit of this method is the short assembly time of these foundations compared to welding. However, some concerns around corrosion, fatigue, and the ability of bolted connections to maintain preload remain. This review found that conventional ring flanges may not be suitable for the assembly of floating foundations, mainly due to the risk of bolt loosening and reduced fatigue life. However, the C1 Wedge Connection is an innovative bolted connection that has shown its ability to retain bolt preload during tests. Likewise, the Compact Flange Connection has shown its ability to retain preload without requiring maintenance during operational stages and furthermore, has a long and successful track record in offshore oil and gas applications. This review revealed several research gaps related to the use of bolted connections for the assembly of floating wind turbine foundations. These include: a lack of research on the effects of bolt loosening; dynamic loads and shear forces on bolted connections and their effect on fatigue life; structural health monitoring methods of bolted connections; and the health and safety of technicians in confined spaces with difficult accessibility. The Compact Flange Connection is perhaps the best suited bolted connection for the assembly of floating foundations. However, more research, and crucially, successful offshore demonstrations will be essential to increase confidence in the suitability of bolted connections for the floating offshore wind industry.

Wave Effects on Large-Scale Turbulent Flow Structures Propagating in the Water Column

Tidal flow often contains large-scale turbulent flow structures mainly caused by bathymetric variations or offshore marine structures. Understanding how waves interact with these structures is crucial for ocean sciences, as they influence vertical mixing, energy transfer, and dissipation. In this work, two flow configurations with current and waves are studied in a flume tank using Particle Image Velocimetry measurements: waves propagate either following or opposing the current and interact with convected flow structures. Compared to current-only cases, the mean velocity is slightly impacted, but the mean velocity gradient increases for waves propagating with the current. Turbulent Kinetic Energy increases regardless of wave direction and its production is also affected by the wave’s propagation direction. The integral length scale and flow Gaussianity are the most affected flow parameters. For waves propagating against the current, the Probability Density Functions of fluctuating velocity fields exhibit a bimodal representation, largely deviating from a Gaussian curve. Preliminary quadrant analysis reveals that waves significantly influence flow organisation, especially when they propagate against the current. These observations are valuable for applications such as defining tidal turbine farm areas, improving turbine performance estimation, and assessing structural fatigue.

Text Mining Approaches for Protein Function Annotation: Challenges and Opportunities

Understanding protein functions is essential for advancing quantitative synthetic biology, which applies quantitative and systems approaches to understand how biological functions emerge from building blocks, thereby guiding the rational design of complex living systems. Apart from a few model organisms, most species contain many proteins with unverified functions, highlighting the need for accurate, automated protein function annotation methods. Recent advances in protein bioinformatics, particularly in predicting structures and functions, have been driven by artificial intelligence (AI), especially deep learning models. Top-performing methods in the Critical Assessment of Function Annotation (CAFA) challenge have leveraged large language models to perform text mining-based protein function prediction, extracting features from scientific literature or using template proteins with similar descriptions in the literature. Despite these advances, several challenges remain. Current predictors often depend on PubMed abstracts curated by UniProt, leading to redundancy with manual annotations and to the overlooking of uncurated or full-text literature that contains richer functional evidence. Few systems automatically classify literature types or assess their relevance, limiting precision and interpretability. Benchmarking remains difficult due to the absence of unbiased gold standards, making it hard to evaluate true predictive capability. Furthermore, integrating heterogeneous evidence—from text, sequences, and structural or network data—presents additional challenges for model harmonization. This review not only summarizes current methods and limitations but also highlights strategies to improve text mining-based protein function annotation using recent AI developments. Overall, this work aims to guide the development of next-generation tools for more accurate and comprehensive protein function predictions.

Visualization of Latent Fingermark on Metallic Surfaces Based on Displacement Reactions

Fingermarks are frequently left on metal surfaces such as kitchen utensils, door handles, or elevator buttons in crime scenes. They are crucial forensic evidence to identify individuals and link them to crimes. Fingermark development on metal surfaces targets either the fingermark residues or the substrate. This study aimed to develop a rapid fingermark development method based on displacement reactions between copper (II) sulphate and various types of metal substrates, such as brass, galvanized iron, and low-carbon steel. Immersion of the metal substrate was more effective in fingermark visualization than applying the solution using a dropper. The optimized concentrations of copper (II) sulphate solution for fingermark visualization were found to be 0.7 M for brass, 0.5 M for galvanized iron, and 0.2 M for low-carbon steel. Sebaceous-rich fingermarks were visualized after the 5th depletion on brass and galvanized iron, and even after the 7th depletion on low-carbon steel. Further improvement is required before incorporating the application of copper (II) sulphate onto metal substrates to visualize fingermarks in real crime cases, due to the destructive nature of substrate submersion.

When Numbers Mislead: Reassessing Statistical Foundations in PCA-Based Prognostication of Fontan Outcomes