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Advances in mucoadhesive and mucus-penetrating materials, nano-formulations, and in situ gelling systems for nasal drug delivery
Full text linkIntroduction
Intranasal drug delivery is increasingly valued not only for local therapy but also as a noninvasive route that can bypass the blood – brain barrier, enabling rapid treatment of neurological and systemic diseases. However, mucociliary clearance and limited epithelial absorption often reduce residence time and bioavailability, creating a need for more effective formulation strategies. Mucoadhesive and mucus-penetrating systems are among the most promising approaches.
Areas covered
This review summarizes nasal anatomical and physiological features that govern interactions between formulations and the mucosa. It overviews representative intranasal dosage forms (liquids, powders, gels, films, in situ gelling systems, and nano-formulations). Polymers used as mucoadhesive agents are classified into first- and second-generation materials, which enhance adhesion through hydrogen bonding, electrostatic interactions, or covalent attachment. The review also highlights polymers applied to nanoparticle surfaces to facilitate diffusion through mucus and improve epithelial access. Finally, methods to evaluate mucoadhesion and toxicity are outlined, including alternative in vitro and in vivo models.
Expert opinion
Recent advances have expanded nasal delivery options, particularly for nose-to-brain targeting. Yet translation remains limited by insufficient validation, long-term safety uncertainties, and repeated-dose effects. Future progress requires balancing adhesion with penetration, robust toxicology, and integration of innovative polymers with optimized devices
Modelling the production impacts of scaling up agroforestry systems in England and Wales
Full text linkCONTEXT
Land managers and governments face the challenge of using finite land, labour, and financial resources to achieve multiple objectives. Agroforestry, the integration of trees with farming, is promoted as a strategy for achieving multiple policy objectives relating to productivity, climate change and biodiversity. However, regional and national-scale studies validating its effectiveness remain limited.
OBJECTIVE
Our study aimed to model the impacts of scaling up agroforestry on food and fibre production in England and Wales. We developed nine agroforestry scenarios combining three tree types (apple, poplar, and short rotation coppice (SRC) willow) at three planting densities. Each scenario scaled agroforestry to all suitable agricultural land (representing 79% of total agricultural land) as a simple modelling objective, rather than a realistic target.
METHODS
We used the well-established Yield-SAFE model to simulate tree-crop/grass interactions at low, medium, and high tree densities, and inputted the resulting yields into the Optimal Land Use Model (OLUM), a linear programming model with the objective of maximising food energy production under defined constraints. The OLUM was validated using baseline data.
RESULTS AND CONCLUSIONS
Scaling up agroforestry increased domestic supply of tree products, for which the UK is heavily import-dependent. However, this came at the expense of the calorific value of food production, which decreased by 3% to 45%, depending on tree type and density. The largest reductions were observed in arable and vegetable crops, due to reduced area and yields. Ruminant livestock outputs were least affected, supported by increases in grassland area. Timber and apple agroforestry systems were predicted to be more land-efficient than equivalent monocultures (for apples, traditional orchards), based on land equivalent ratios.
SIGNIFICANCE
Upscaling agroforestry could reduce import reliance on tree products while contributing to environmental objectives. To minimise reductions in domestic food supply, policymakers should prioritise agroforestry in pasture-based systems and support wider food system changes. Design improvements could lessen trade-offs associated with tree planting in arable systems. Future research should investigate how scaling up agroforestry systems affects greenhouse gas emissions, biodiversity, soil, water, and the general resilience of the farmed landscape
Financial risks of biodiversity loss: a review
Full text linkIn this paper, we review the burgeoning literature on financial risks stemming from biodiversity loss. To build our literature database, we systematically collect 444 journal articles and preprints written in English and published between 1989 and 2024, retrieved from Scopus. The selection focuses on studies in the fields of business, economics, and finance, identified through a transparent and reproducible query-based approach. We contribute to the literature by providing the first comprehensive synthesis of the emerging literature on biodiversity-related financial risks. Specifically, we compare existing biodiversity loss metrics and evaluate their strengths and limitations for finance research, while also reviewing evidence on firms’ and financial systems’ exposure to biodiversity physical and transition risks, as well as the influence of these risks on asset pricing. The review underscores that no single metric “outperforms” the others; rather, valuable insights arise from examining multiple indicators, with the choice of metric depending on the specific analytical objective. It also shows that the financial sector is highly, albeit indirectly, exposed to biodiversity physical risk, and that firms – and their value chains – exert substantial impacts on biodiversity. In addition, the review of the literature highlights that investors are increasingly demanding higher premiums to firms exposed to biodiversity risks, suggesting that financial markets deem such risks material. Finally, we identify key research gaps and discuss potential future developments for this field of study, outlining a possible research agenda on biodiversity-related financial risks
Supplementary light intensity and harvest date affect midrib oxidative pinking and related metabolites in two Romaine lettuce cultivars with contrasting discolouration sensitivities
Full text linkThis study elucidates the variations in phenolic acids, soluble sugars, and pinking development of midribs of two cultivars of Romaine lettuce (Keona—high pinking and Icarus—low pinking) under two light intensities (high L1—558 and low L2—244 µmol m−2 s−1) harvested at two harvest dates (M1—42 and M2—49 days after transplanting, DAT). The pinking index of Keona was higher than that of Icarus on 8 days of storage (5 °C). The concentrations of cinnamic acid were reduced in most treatments for both cultivars during storage, except for Keona grown in L2 with M2 harvest. Upon storage, the concentrations of coumaric acid in Keona were similar regardless of light intensities and harvest dates. Coumaric acid and caffeic acid concentrations in Icarus in L1 harvested at M2 were the highest. Low light intensity with M1 harvest enhanced the concentration of chlorogenic acid in Keona, but a similar situation reduced its content in Icarus during storage. Icarus contained higher initial concentrations of glucose under both light intensities, regardless of harvest dates, compared to Keona. In conclusion, high pinking was associated with high phenolic acids except for cinnamic acid. High light intensities and more advanced harvests increased the pinking of Keona but not of the Icarus
ClimaLand: a land surface model designed to enable data-driven parameterizations
No full textLand surface models (LSMs) are essential tools for simulating the coupled climate system, representing the dynamics of water, energy, and carbon fluxes on land and their interaction with the atmosphere. However, parameterizing sub-grid processes at the scales relevant to climate models (
10–100 km) remains a considerable challenge. The parameterizations typically have a large number of unknown and often correlated parameters, making calibration and uncertainty quantification difficult. Moreover, many existing LSMs are not readily adaptable to the incorporation of modern machine learning (ML) parameterizations trained with in situ and satellite data. This article presents the first version of ClimaLand, a new LSM designed for overcoming these limitations, including a description of the core equations underlying the model, the results of an extensive set of validation exercises, and an assessment of the computational performance of the model. We show that ClimaLand can leverage graphics processing units for computational efficiency, and that its modular architecture and high-level programming language, Julia, allows for integration with ML libraries. In the future, this will enable efficient simulation, calibration, and uncertainty quantification with ClimaLand
Semantic processing in autism during speech-in-music listening: insights from congruency and surprisal-based N400 analyses
No full textUnderstanding speech in background music is a common real-world challenge, particularly when vocals compete for linguistic processing resources. This study examined how the presence and intelligibility of sung lyrics influence semantic processing in autistic and non-autistic adults. Twenty-nine participants per group performed a sentence acceptability judgement task while EEG was recorded. Sentences ended with either semantically congruent or incongruent words and were presented alongside instrumental, Simlish (phonologically English-like but unintelligible), or English-lyric versions of the same songs. To examine semantic processing, we analysed the N400 using two complementary approaches: a categorical congruency contrast, indexing the neural cost of processing semantic anomalies, and a continuous lexical surprisal measure, capturing graded sensitivity to word predictability. In non-autistic participants, both analyses showed largest N400 responses in the instrumental condition, attenuated responses in vocal conditions, and reduced behavioural accuracy as lyrics became more intelligible. Autistic participants showed lower accuracy and a reduced N400 effect relative to non-autistic participants, particularly in the instrumental music condition. In addition, they exhibited no behavioural difference between the English and Simlish vocal conditions, suggesting that changes in lyric intelligibility did not affect accuracy. By combining ecologically valid speech-in-music masking with dual analytic approaches, this study provides the first neurophysiological evidence of these semantic processing differences in autism and demonstrates how integrating categorical and probabilistic measures can yield a richer and more nuanced account of speech processing in complex auditory environments
Highlights of model quality assessment in CASP16
Full text linkModel quality assessment (MQA) remains a critical component of structural bioinformatics for both structure predictors and experimentalists seeking to use predictions for downstream applications. In CASP16, the Evaluation of Model Accuracy (EMA) category featured both global and local quality estimation for multimeric assemblies (QMODE1 and QMODE2), as well as a novel QMODE3 challenge—requiring predictors to identify the best five models from thousands generated by MassiveFold. This paper presents detailed results from several leading CASP16 EMA methods, highlighting the strengths and limitations of the approaches
An evidence-based framework for NATO’s human security approach
No full textNATO adopted its Human Security Approach and Guiding Principles at the Madrid Summit of 2022. Whilst NATO identifies human security as an “essential tool” and seeks to integrate it to a degree sufficient to shape its core tasks, the Guiding Principles lack depth; they are underdeveloped, fail to make clear the distinctive nature of the concept of human security, the evidence base on which it is founded, or the difference it can make to the achievement of the organization’s goals. Discussing NATO’s emerging shift to a human security approach, this article provides a framework of human security based on six evidence-based principles that provide additional depth and clarity to NATO’s approach. The framework can be used by NATO and its personnel, such as Human Security Advisors, Military Police and Civil-Military Cooperation (CIMIC) officers, to develop the military contribution to human security (MC2HS)
The potential of plasma-processed air (PPA) for industrial food safety in herbs and spices
Full text linkNon-thermal plasma is gaining attention for food decontamination due to effective microbial inactivation, minimal quality deterioration, reduced energy consumption compared with thermal treatment. However, the lack of upscaled equipment suitable for industrial production has limited its transfer from lab to industry. The present study aims to validate the production efficiency of reactive species of an upscaled microwave plasma source – _PLexc2. Fourier transformation infrared (FTIR) spectroscopy was applied for the chemical characterization and quantification of long-living reactive species in PPA. PLexc2 could deliver 15,412, 13,263, and 11,522 ppm of NOx (sum of NO2 and NO) at flow rates of 63, 73, and 83 L/min, respectively. Throughout a 60- min PLexc2 operation, the temperature in the treatment chamber remained within the range of room temperature (20–25 ◦C). PPA treatment was then tested against Enterococcus faecium inoculated on crushed oregano leaves. For 1- and 30-min PPA treatments, 1.90 log10 and 4.49 log10 reductions were achieved, respectively. One hour of post-discharge sample incubation in the treatment chamber, in addition to a 1-min PPA treatment, increased the reduction from 1.90 log10 to 3.21 log10. The economic feasibility was assessed by estimating conversion costs of PPA treatment, which ranged from 0.04 to 80.60 €/kg depending on treatment mode and scale. The return on capital investment of PLexc2 equipment was estimated to be less than one year for a production quantity of 2000 t/yr. In conclusion, this study demonstrates the potential and the economic feasibility of applying an upscaled microwave plasma source in the food industry