Promoting global collaboration to improve bioaerosol exposure assessment and understanding of associated health impacts: outcomes from a series of workshops

We are surrounded, in both indoor and outdoor environments, by air containing particles of biological origin (bioaerosols). We constantly inhale them, and, depending upon their size, they deposit in different parts of our airways. Despite their ubiquitous nature and our constant exposure, bioaerosol diversity and composition of the environment are not well characterized, and we understand little about which bioaerosols we are exposed to and how this impacts our health, either positively or negatively. Indoor/Outdoor Bioaerosols Interface and Relationships Network (BioAirNet), a Clean Air Programme-funded network, has recognized the need for the bioaerosol community to reflect on the current challenges facing bioaerosol exposure assessment and the determination of the associated cellular/molecular responses driving specific health outcomes. A series of online workshops for the bioaerosol community were hosted by BioAirNet in September 2022, which aimed to bring together global expertise to discuss the current challenges impeding improved assessment of bioaerosol exposure and understanding of the downstream cellular and molecular mechanisms driving health outcomes by discussing these challenges; considering where we need to be, where we are now and how we get there. Professional facilitation was key to their success, enabling the multidisciplinary bioaerosol community to explore and address these challenges within a focused and productive environment to prioritize themes and agree on action plans for continued momentum following the workshops. These themes were as follows: (1) conceptual model; (2) stakeholder mapping; (3) knowledge transfer; (4) writing project and (5) conference-type event, collectively covering research, knowledge mobilization and networking activities. A subsequent in-person follow-up workshop was held in November 2023. It provided an opportunity to share progress on the five themes, critique what had already been done and act as a launch-pad to progress the actions further. Delegates also had the opportunity to share ongoing or upcoming work, particularly projects requiring input from others, to encourage collaborative working and sharing expertise. The use of facilitated workshops is a valuable tool for all scientific communities to collectively explore and successfully address key issues within their field

Optimizing In Vitro Skin Permeation Studies to Obtain Meaningful Data in Topical and Transdermal Drug Delivery

Drug delivery through the skin provides several advantages over other administration routes, including the avoidance of first-pass metabolism and gastrointestinal side effects, prolonged drug release, and significant improvement in patient compliance. It is imperative to study the in vitro behavior of drugs and formulations before proceeding to in vivo evaluations. As the ethical guidelines for scientific research evolve, there is an increasing emphasis on adopting alternative methods to reduce animal use. An in vitro permeation study (IVPT) estimates the rate and extent of drug permeation from a topical or transdermal delivery, determining its availability at the skin layers or into the systemic circulation. Vertical Franz diffusion cells are commonly employed for IVPT studies to evaluate the permeation of drugs across skin or other biorelevant membranes. This comprehensive review provides a clear understanding of the importance of optimizing in vitro experimental conditions to obtain reliable and reproducible data. We discuss various in vitro skin models, including excised human and animal skins, human skin equivalents (HSEs), synthetic membranes, and 3D-printed skin models. Additionally, a broad overview of setting up in vitro diffusion cells is provided. Emphasis is given on donor phase design, receptor medium selection, the importance of solubility and stability studies, sampling techniques, and analysis methods. Meticulous design and optimization of in vitro permeation experiments are crucial for generating reproducible data, which are essential for predicting the dermatokinetics of drugs and formulations

Secure Identity Management System in Unmanned Aerial Vehicles Network

In recent years, rapid advancements in digital transformation and communication technologies have led to the widespread adoption of autonomous systems, particularly Unmanned Aerial Vehicles (UAVs), in societal and industrial applications. The integration of smart cities, the Internet of Things (IoT), and 5G technologies has enabled UAVs to be utilized effectively in more complex and dynamic tasks. For instance, during the COVID-19 pandemic, UAVs played critical roles in maintaining social distancing, delivering medical supplies, and managing crowds. Such contemporary applications have once again highlighted the importance and potential of UAV networks. The flexibility and versatility offered by UAVs facilitate the development of innovative solutions across a wide spectrum—from agriculture to logistics, disaster management to security. Specifically, swarm UAV systems surpass the limitations of individual vehicles, providing advantages such as real-time data collection, large-scale monitoring, and the parallel execution of complex tasks.However, the effective and secure operation of such systems depends on the reliability and efficiency of intra-network communication and identity management protocols. In today's cyber-physical systems, security threats and cyber-attacks are becoming increasingly sophisticated. UAV networks are not exempt from these threats; risks such as identity spoofing, data manipulation, and Denial-of-Service (DoS) attacks endanger the success and security of operations. Addressing these security vulnerabilities is of vital importance, especially in sensitive areas like the protection of critical infrastructures, border security, and emergency interventions. This thesis aims to enhance the operational efficiency and security of UAV networks by developing a lightweight and dynamic identity management protocol alongside a consensus mechanism specifically optimized for UAV networks. The proposed identity management protocol employs symmetric cryptography and hash functions, featuring low computational and communication overhead while adapting to dynamic network topologies. The protocol is resilient against common security threats such as identity spoofing, replay attacks, and man-in-the-middle attacks.Furthermore, leveraging the advantages of blockchain technology, a fast and efficient consensus mechanism suitable for UAV networks has been designed. Instead of energy-intensive and high-latency methods like traditional Proof of Work (PoW), an adapted version of the Practical Byzantine Fault Tolerance (PBFT) algorithm and a Fuzzy C-Means Clustering algorithm (FCMCA) are utilized to reduce latency and computational costs. This mechanism enables secure and effective data sharing and decision-making processes among UAVs. Simulations and performance analyses have demonstrated that the proposed solutions provide lower latency and reduced resource consumption compared to existing methods, while exhibiting high resilience against security threats. These findings contribute significantly to the safer, more efficient, and scalable use of UAV networks in real-world applications. The study aims to establish a solid foundation for the evolution and sustainability of UAV networks and serves as a valuable reference for future technological developments and applications

A Survey of the Current UK Physician Associate Educator Workforce and Recommendations for Courses and Provider Organizations

In the United Kingdom there are 37 physician associate (PA) training programs with limited knowledge of the educators involved, their training, and specific needs. An online questionnaire was sent to PA educators in all UK training programs requesting information on academic title and responsibilities, clinical and nonclinical background, education and qualifications before becoming a PA educator, formal and informal training received in the role, and insights into career progression. The questionnaire highlighted 5 specific areas that should be specific recommendations for UK training programs to support PA educators, alongside existing guidance. These centered on academic and leadership development, clinical engagement, student support, and pedagogical research. We believe that implementing these recommendations across training programs will improve the opportunities for all those delivering PA education and consequently improve the offering to the students undertaking PA studies programmes. [Abstract copyright: Copyright © 2024 PA Education Association.

Understanding and predicting animal movements and distributions in the Anthropocene

Predicting animal movements and spatial distributions is crucial for our comprehension of ecological processes and provides key evidence for conserving and managing populations, species and ecosystems. Notwithstanding considerable progress in movement ecology in recent decades, developing robust predictions for rapidly changing environments remains challenging. To accurately predict the effects of anthropogenic change, it is important to first identify the defining features of human‐modified environments and their consequences on the drivers of animal movement. We review and discuss these features within the movement ecology framework, describing relationships between external environment, internal state, navigation and motion capacity. Developing robust predictions under novel situations requires models moving beyond purely correlative approaches to a dynamical systems perspective. This requires increased mechanistic modelling, using functional parameters derived from first principles of animal movement and decision‐making. Theory and empirical observations should be better integrated by using experimental approaches. Models should be fitted to new and historic data gathered across a wide range of contrasting environmental conditions. We need therefore a targeted and supervised approach to data collection, increasing the range of studied taxa and carefully considering issues of scale and bias, and mechanistic modelling. Thus, we caution against the indiscriminate non‐supervised use of citizen science data, AI and machine learning models. We highlight the challenges and opportunities of incorporating movement predictions into management actions and policy. Rewilding and translocation schemes offer exciting opportunities to collect data from novel environments, enabling tests of model predictions across varied contexts and scales. Adaptive management frameworks in particular, based on a stepwise iterative process, including predictions and refinements, provide exciting opportunities of mutual benefit to movement ecology and conservation. In conclusion, movement ecology is on the verge of transforming from a descriptive to a predictive science. This is a timely progression, given that robust predictions under rapidly changing environmental conditions are now more urgently needed than ever for evidence‐based management and policy decisions. Our key aim now is not to describe the existing data as well as possible, but rather to understand the underlying mechanisms and develop models with reliable predictive ability in novel situations

Determining absolute neutrino mass using quantum technologies

Next generation tritium decay experiments to determine the absolute neutrino mass require high-precision measurements of β-decay electron energies close to the kinematic end point. To achieve this, the development of high phase-space density sources of atomic tritium is required, along with the implementation of methods to control the motion of these atoms to allow extended observation times. A promising approach to efficiently and accurately measure the kinetic energies of individual β-decay electrons generated in these dilute atomic gases, is to determine the frequency of the cyclotron radiation they emit in a precisely characterised magnetic field. This cyclotron radiation emission spectroscopy (CRES) technique can benefit from recent developments in quantum technologies. Absolute static-field magnetometry and electrometry, which is essential for the precise determination of the electron kinetic energies from the frequency of their emitted cyclotron radiation, can be performed using atoms in superpositions of circular Rydberg states. Quantum-limited microwave amplifiers will allow precise cyclotron frequency measurements to be made with maximal signal-to-noise ratios and minimal observation times. Exploiting the opportunities offered by quantum technologies in these key areas, represents the core activity of the Quantum Technologies for Neutrino Mass (QTNM) project. Its goal is to develop a new experimental apparatus that can enable a determination of the absolute neutrino mass with a sensitivity on the order of 10~meV/c2

Multi-objective Bayesian shape optimization of an industrial hydrodynamic separator using unsteady Eulerian-Lagrangian simulations

The shape of a hydrodynamic particle separator has been optimized using a parallelized and robust formulation of Bayesian optimization, with data from an unsteady Eulerian flow field coupled with Lagrangian particle tracking. The uncertainty due to the mesh, initial conditions, and stochastic dispersion in the Eulerian-Lagrangian simulations was minimized and quantified. This was then translated across to the error term in the Gaussian process model and the minimum probability of improvement infill criterion. An existing parallelization strategy was modified for the infill criterion and customized to prefer exploitation in the decision space. In addition, a new strategy was developed for hidden constraints using Voronoi penalization. In the approximate Pareto Front, an absolute improvement over the base design of 14% in the underflow collection efficiency and 10% in the total collection efficiency was achieved, which resulted in the filing of a patent.* The corresponding designs were attributed to the effective distribution of residence time between the trays via the removal of a vertical plume. The plume also reduced both efficiencies by creating a flow path in a direction that acted against effective settling. The concave down and offset tray shapes demonstrated the value of Bayesian optimization in producing useful and non-intuitive designs

The Effect of Endurance Exercise and its Intensity in Middle- aged Runners; Are they Thrombogenic?

Introduction Despite the well documented benefits of regular exercise, acute exercise induces a transient hypercoagulable state with increasing risk of thrombotic disease with age and intensity. While prior studies have used various conventional coagulation tests in studying the influence of exercise on coagulation, limited attention has been given to clot microstructure and contraction profile in well-trained individuals of middle to older age. Our aim was to identify effects of exercise on these variables using hemorheological biomarkers. Materials and methods Twenty-eight male and female runners aged over 40 years completed a 10 km run at moderate intensity. Of these runners,14 were reinvited to complete a 3 km run to exhaustion. Blood samples were drawn at three time-points, baseline, immediately after exercise and after 1 hour of recovery. Structural biomarker df and measurements of mature clot mechanical properties (Maximum Contractile Force and G’Max) were analysed alongside conventional coagulation markers. Results While df remained stable following long moderate intensity exercise, higher intensity exercise caused an increase in df indicating a hypercoagulable phase. Following an hour of rest, df returned to baseline. These results indicate that the effect of acute exercise on hypercoagulability is intensity dependent and transient. Maximum Contractile Force (CFMax) was reduced by exercise, irrespective of intensity. This effect was lower after an hour of rest, suggesting that some unknown initial compensatory mechanisms are outlasted by a longer period of reduced contractile force. Conclusion df and CFMax detected the hypercoagulable phase that occurred in trained older individuals as a result of exercise. Investigating these effects in more sentient populations could allow risk stratification of exercise rehabilitation programmes and their intensity

Enhancing Fairness, Justice and Accuracy of Hybrid Human-AI Decisions by Shifting Epistemological Stances

From applications in automating credit to aiding judges in presiding over cases of recidivism, deep-learning powered AI systems are becoming embedded in high-stakes decision-making processes as either primary decision-makers or supportive assistants to humans in a hybrid decision-making context, with the aim of improving the quality of decisions. However, the criteria currently used to assess a system’s ability to improve hybrid decisions is driven by a utilitarian desire to optimise accuracy through a phenomenon known as ‘complementary performance’. This desire puts the design of hybrid decision-making at odds with critical subjective concepts that affect the perception and acceptance of decisions, such as fairness. Fairness as a subjective notion often has a competitive relationship with accuracy and as such, driving complementary behaviour with a utilitarian belief risks driving unfairness in decisions. It is our position that shifting epistemological stances taken in the research and design of human-AI environments is necessary to incorporate the relationship between fairness and accuracy into the notion of ‘complementary behaviour’, in order to observe ‘enhanced’ hybrid human-AI decisions