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Nutraceuticals for gut–brain axis health: a novel approach to combat malnutrition and future personalised nutraceutical interventions
Get PDFThe gut–brain axis (GBA) is a bidirectional communication network between the gastrointestinal tract and the brain, modulated by gut microbiota and related biomarkers. Malnutrition disrupts GBA homeostasis, exacerbating GBA dysfunction through gut dysbiosis, impaired neuroactive metabolite production, and systemic inflammation. Nutraceuticals, including probiotics, prebiotics, synbiotics, postbiotics, and paraprobiotics, offer a promising approach to improving GBA homeostasis by modulating the gut microbiota composition and related neuroactive metabolites. This review aims to elucidate the interplay between gut microbiota-derived biomarkers and GBA dysfunction in malnutrition and evaluate the potential of nutraceuticals in combating malnutrition. Furthermore, it explores the future of personalised nutraceutical interventions tailored to individual genetic and microbiome profiles, providing a targeted approach to optimise health outcomes. The integration of nutraceuticals into GBA health management could transform malnutrition treatment and improve cognitive and metabolic health
Effect of Astragalus mollissimus on ruminal fermentation, methane production and performance of sheep
Get PDFRecent studies have shown the anti‐methanogenic capacity of Astragalus mollissimus (AM), a plant found in semiarid environments, which is known to produce 3‐nitro‐1‐propionic acid (3NPA) and 3‐nitropropanol (3NPOH). However, little is known about the effects of direct supplementation in basal diets, given that it is also known to cause cattle poisoning by nitro toxins in rangelands. In the present study, two experiments were carried out to determine CH4 and volatile fatty acid production, animal performance and the presence of nitrocompounds in blood. In Experiment 1, four Pelibuey sheep (BW 52.8 ± 6.05 kg) were assigned to a 4 × 4 Latin square arrangement. In Experiment 2, 20 Dorper sheep were randomized to five treatments. In both experiments, AM was supplemented and fully homogenized into diets consisting of 67% oat hay and 33% concentrate. The supplementation with different amounts of AM reduces (p ≤ 0.05) the total gas and methane production. Methane was reduced by 60% when 1 g AM kg−1 BW day−1 was supplemented. No effects (p > 0.05) were observed in feed consumption and average daily gain. However, feed conversion was increased (p < 0.05) with AM supplementation. Finally, no differences (p > 0.05) were observed in nitrocompound concentration in plasma. These results demonstrate that 3NPA and 3NPOH from biological sources possess desirable anti‐methanogenic properties to be considered supplementation alternatives
Impact of WIVERN wind observations on ARPEGE numerical weather prediction model forecasts using an ensemble of data assimilation method
No full textTo fill the gap of in‐cloud wind observations in the global observing system, the European Space Agency selected the wind velocity radar nephoscope (WIVERN) mission as one of the Earth Explorer 11 candidate missions to enter Phase A in 2023. WIVERN, with its dual‐polarisation Doppler conically scanning W‐band radar, will be the first space‐based mission to provide in‐cloud horizontal line‐of‐sight (HLOS) winds at a fine vertical resolution of 650 m sampling and a broad swath of width 800 km. We report on the impact of WIVERN simulated HLOS winds to improve global numerical weather prediction (NWP) model forecasts, using an ensemble of data assimilation (EDA) approach. In this methodology, the benefits of adding WIVERN simulated HLOS wind observations to the current observing system are measured by their ability to reduce the EDA spread at a given forecast lead time. The operational EDA system of the global NWP model ARPEGE (Action de Recherche Petite Echelle Grande Echelle) is used for a 1‐month period in 2021. Results indicate that WIVERN HLOS will not only significantly improve the uncertainty of the wind forecasts throughout the entire troposphere, but also of the temperature and humidity fields. This positive impact is particularly seen in the midlatitudes. Results of this study also highlight the strong vertical complementarity between WIVERN, Aeolus (Doppler wind lidar), and atmospheric motion vectors observations. Finally, the impact of WIVERN is also studied in synergy with the EUMETSAT follow‐on EPS‐Aeolus mission, and results demonstrate that the two active wind satellite missions would vertically complement each other as they provide wind observations at different altitudes, and in different meteorological areas
Energetically consistent localised APE budgets for local and regional studies of stratified flow energetics
Get PDFBecause it allows a rigorous separation between reversible and irreversible processes, the concept of available potential energy (APE) has become central to the study of turbulent stratified fluids. In ocean modelling, it is fundamental to the parameterisation of meso-scale ocean eddies and of the turbulent mixing of heat and salt. However, how to apply APE theory consistently to local or regional subdomains has been a longstanding source of confusion due to the globally defined Lorenz reference state entering the definition of APE and of buoyancy forces being generally thought to be meaningless in those cases. In practice, this is often remedied by introducing heuristic `localised' forms of APE density depending uniquely on region-specific reference states, possibly diverging significantly from the global Lorenz reference state. In this paper, we argue that across-scale energy transfers can only be consistently described if
localised forms of APE density are defined as the eddy APE component of an exact mean/eddy decomposition of the APE density, for which a new physically more intuitive and mathematically simpler framework is proposed. The eddy APE density thus defined exhibits a much weaker dependency on the global Lorenz reference state than the mean APE, in agreement with physical intuition, but with a different structure than that of existing heuristic localised APE forms. Our framework establishes a rigorous physical basis for linking parameterised energy transfers to molecular viscous and
diffusive dissipation rates. We illustrate its potential usefulness by discussing the energetics implications of
standard advective and diffusive parameterisations of the
turbulent density flux, which reveals potential new sources of
numerical instability in ocean models
Business incubators as institutional intermediaries in emerging countries: the case study of Kazakhstan
Get PDFKazakhstan is currently undergoing a significant economic transition, shifting from
resource dependency toward fostering innovation and entrepreneurship. Business incubators
are central to this diversification strategy, serving as institutional intermediaries that bridge
critical gaps in resources, networks, and expertise for early-stage ventures. This study explores
the role of business incubators in facilitating entrepreneurship within Kazakhstan’s
institutionally void environment. The study uses institutional theory as a framework to consider
how structural and contextual factors affect the way incubators work, with a focus on how they
can change institutions. We collected data for this qualitative study through 66 semi-structured
interviews with managers, incubatees, and policymakers from two prominent Kazakhstani
incubators—MOST Inc. and NURIS—and analysed the data via thematic analysis and
inductive reasoning techniques.
Analysis identified key structural challenges impacting incubators: limited funding,
inadequate infrastructure, and weak institutional linkages. In this context, we found that
business incubators take on dual roles, protecting entrepreneurs from external risks and
bridging resource and network gaps. These roles themselves play a crucial role during the
critical period when early-stage ventures are most susceptible to failure. The study also
identified the dynamic influence of cultural and regulatory factors on incubators’ ability to
foster entrepreneurial success. Findings influenced the development of a context-specific
framework for optimising business incubators in emerging economies. This framework
integrates the inputs, processes, and outputs (IPO) model with business lifecycle stages and
ecosystem dynamics, offering actionable insights for policymakers and stakeholders.
The study concludes that structural barriers and institutional voids constrain the
effectiveness of business incubators, despite their pivotal role in Kazakhstan’s entrepreneurial
ecosystem. To maximise their impact, targeted policies and ecosystem development strategies
are required. This research contributes to institutional theory and offers practical
recommendations for enhancing business incubation in emerging economies
CMIP7 data request: impacts and adaptation priorities and opportunities
No full textThe Coupled Model Intercomparison Project Phase 7 (CMIP7) undertook an extensive process to gather community input and refine data requests related to impacts and adaptation applications of Earth System Model (ESM) outputs. The Impacts and Adaptation (I&A) Data Request Team worked with CMIP7 leadership to distribute an open solicitation across many communities that use climate model outputs requesting inputs for new and existing variables, the most applicable temporal characteristics, and groupings of variables that together allow for specific application opportunities. This input was then collated and translated into CMIP7 standard templates for inclusion in the broader data request, leading to 13 I&A data request opportunities, 60 variable groups and 539 unique variables sought by vulnerability, impacts, adaptation, and climate services user communities. Here, we describe these opportunities and variable groups, as well as new insights into how ESM groups can prioritize outputs that set off a chain of further analyses, ultimately informing decisions impacting society and natural systems. These include an emphasis on high-resolution outputs to allow further modeling of climate impacts at regional and local scales, improved representation of extreme weather events, enhanced accuracy of downscaling and bias-adjustment techniques, and support for more detailed assessments for decision-making in adaptation and mitigation strategies. There is also broad interest in more extensive provisioning of two-dimensional variables at the Earth’s surface, prioritizing experiments that enhance our understanding of both the recent past and future scenarios, and providing outputs that allow further downscaling and bias adjustment. We emphasize that variable groups are the fundamental level at which to engage with the I&A data request, matching the scale of input and the way output provision enables specific I&A applications. Given resource constraints, we applaud CMIP7 efforts to foster strong engagement and communication between ESM groups and the I&A team to build consensus around prudent compromises in priority variables, temporal resolutions, simulation experiments, time subsets, and ensemble members
The bad, the very bad and the ugly: towards an integrated model of dark leadership
No full textPurpose – Over the last decade, there has been a growing interest in the effects of bad leadership on
organisations and employees. In part, this can be seen as being associated with the continuing emergence of
corporate scandals and high-profile cases of mistreatment of employees. In this paper, we present a systematic
review and critique of the literature that explores dark leadership over the last 24 years.
Design/methodology/approach – The approach adopted employed a systematic literature review followed by
an analysis of the key themes, findings and gaps in the literature.
Findings – The literature is diverse and often confusing, with multiple terms, conceptualisations, absence of
shared theoretical underpinning and measurement scales for similar phenomena. We identify the major gaps and
challenges within the literature and conclude by presenting a potential model that integrates the dark leadership
literature within a clear theoretical framework.
Research limitations/implications – The review has the limitation that papers not matching the search criteria
may have been missed. However, the risk was ameliorated by using a broad range of search terms.
Practical implications – Developing a clear understanding of the nature and consequences of dark leadership
will be of value to organisations in terms of being able to develop strategies that avoid its emergence and
negative consequences.
Social implications – Bringing attention to the nature and consequences of dark leadership may lead to actions
to avoid its emergence or reduce its impact. This in turn could lead to reduced damage to the well-being of
employees in organisations.
Originality/value – The paper integrates over 20 years of literature, providing a clear framework for future
research and highlighting how personality traits, specifically the dark triad, act as foundational antecedents of
dark leadership behaviours.
Keywords Dark leadership, Dark triad, Toxic leadership, Destructive leadership, Abusive supervision,
Personality
Paper type Literature revie
Trump’s claim that US debt calculation may be fraudulent could put the economy in danger
No full textImproved clinical care and capacity through an integrated electronic patient reported outcome measure and health record system in inflammatory arthritis
Get PDFObjectives Patient-reported outcome measures (PROMs) are essential for inflammatory arthritis (IA). Collecting electronic PROMs remotely into electronic patient records (EPRs) enables timely, patient-centred care. This study evaluated the real-world clinical impact, efficiency, and feasibility of implementing a fully integrated ePROM system across three IA conditions: rheumatoid arthritis, axial spondyloarthritis and psoriatic arthritis. Methods From January 2019 to December 2024, IA patients completed ePROMs remotely at regular intervals or on request by patients or clinicians, with data automatically uploaded to EPR. Alerts based on predefined thresholds prompted clinical review. Patients with stable disease were transitioned to patient-initiated follow up. Outcomes include ePROM completion rates, patient engagement, satisfaction, appointment utilisation, time savings and the new-to-follow up (N: FU) ratio. Time saved was estimated by calculating reduced follow-up appointments multiplied by 20-minute consultation length. We assessed effectiveness through patient and clinician engagement and satisfaction. Results The ePROM completion rates improved from 25% (paper) to 66% (ePROM). A total of 1500 clinic hours per year were saved through reduced follow ups. The N: FU ratio improved from 1:3.1 in 2019 to 1:2.2 in 2024. Increased capacity enabled shorter waiting times for new and urgent follow up patient clinic appointments. Longitudinal ePROM trends provided better actionable insight than single time points. Conclusion The integrated ePROM system enhanced PROM completion, enabled safe remote monitoring and supported expansion of PIFU. The system improved the efficiency and responsiveness of IA care as well as promoted personalised care
Capturing local compositional fluctuations in NMR modelling of solid solutions
Get PDFUnderstanding the atomic-scale local properties of solid solutions is crucial for deciphering their structure–property relationships. In this work, we present a computational approach that combines solid-state nuclear magnetic resonance (NMR) spectroscopy with density functional theory (DFT) calculations to investigate local chemical environments in solid solutions. Previous canonical ensemble models, which only sample configurations at a fixed composition of the simulation cell, fail to capture local compositional fluctuations that can significantly influence the NMR spectra. To address this limitation, we employ a grand-canonical ensemble approach enabling a more comprehensive representation of the contributions of all possible local chemical environments to the NMR spectrum, using a La2(Zr1−xSnx)2O7 pyrochlore solid solution as a case study. To mitigate the high computational cost of such simulations, we also explore ensemble truncation strategies and the use of machine learning (ML) to aid predictions of NMR chemical shifts, achieving a significant reduction in computational cost while maintaining most of the predictive power. Our results show that combining the grand-canonical approach with machine learning and ensemble truncation offers an efficient framework for modelling and interpreting NMR spectra in disordered crystalline materials