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PubMed2025

OpenSpliceAI: An efficient, modular implementation of SpliceAI enabling easy retraining on non-human species.

The SpliceAI deep learning system is currently one of the most accurate methods for identifying splicing signals directly from DNA sequences. However, its utility is limited by its reliance on older software frameworks and human-centric training data. Here we introduce OpenSpliceAI, a trainable, open-source version of SpliceAI implemented in PyTorch to address these challenges. OpenSpliceAI supports both training from scratch and transfer learning, enabling seamless retraining on species-specific datasets and mitigating human-centric biases. Our experiments show that it achieves faster processing speeds and lower memory usage than the original SpliceAI code, allowing large-scale analyses of extensive genomic regions on a single GPU. Additionally, OpenSpliceAI's flexible architecture makes for easier integration with established machine learning ecosystems, simplifying the development of custom splicing models for different species and applications. We demonstrate that OpenSpliceAI's output is highly concordant with SpliceAI. In silico mutagenesis (ISM) analyses confirm that both models rely on similar sequence features, and calibration experiments demonstrate similar score probability estimates.

Journal Article↗
PubMed2001

[Increase in strength after active therapy in chronic low back pain (CLBP) patients: muscular adaptations and clinical relevance].

INTRODUCTION: Active treatments are advocated for the management of non-specific chronic low back pain (CLBP), although few studies have documented the relative efficacy of differing types of programme. A number of the available treatments comprise exercise routines on specially designed training machines, which are ostensibly better disposed to reverse the compromised trunk muscle function displayed by these patients than are 'free exercise' programmes. However, in using these muscle-training programmes, the physiological or anatomical adaptations that might account for the improved performance are rarely investigated, let alone identified. This is an important issue, because if the 'newly-acquired strength' is mostly specific to performance on the devices on which the patient has trained and been tested, and reflects the skill in executing these particular tasks, this will not necessarily assist the patient during performance of his/her everyday activities. The aims of the present study were (1) to quantify the changes in back muscle performance in chronic LBP patients following 3 months active therapy, and (2) to analyse the corresponding changes in activation and cross-sectional area of the paraspinal muscles. METHODS: 148 individuals (57% women) with CLBP (age 45.0+/-10.0 years; duration of LBP 10.9+/-9.5 years) were randomised to a treatment which they attended 2/week for 3 months: active physiotherapy, muscle reconditioning on training devices, or low-impact aerobics. Pre- and post-therapy, assessments were made of isometric trunk muscle strength in each plane of movement and of erector spinae activation (using surface electromyography) during back extension. In a sub-group of 56 patients, the cross-sectional area of the paravertebral muscles was determined using magnetic resonance imaging (MRI). In all patients, self-rated pain intensity, pain frequency and disability were assessed before and after therapy. RESULTS: 132/148 patients completed the therapy. Isometric strength in each movement plane increased significantly in all groups post-therapy. Apart from trunk extension, the changes were significantly greater in the devices group than in the other two groups (Fig 1). Activation of the paraspinal muscles during back extension also increased significantly in all groups (Fig 2) and was weakly, but significantly (r = 0.37; p = 0.0001) correlated with increased strength in back extension. Although, at baseline, highly significant correlations were observed between the size of the paraspinal muscles (at L3/4 and at L4/5) and isometric back extension strength (r=0.75; p< 0.0001), post-training increases in strength were not accompanied by corresponding changes in muscle size. None of the improvements in strength showed any relationship with the clinical changes in pain and disability, regardless of whether the latter were examined on an individual basis or in relation to 'outcome groups'. CONCLUSION: The superior trunk strength shown by the devices group post-therapy was considered to be attributable, in part, to a 'learning effect', of the type often seen when training and testing are carried out on the same machines. These gains are considered to be mostly 'task-specific'. However, part of the improvement in strength after active therapy (in all groups) also appeared to be due to an increased neural activation of the trunk muscles. These positive effects should be transferable to the performance of everyday activities for which the same muscles are employed, although the percentage improvement is probably not as high as the measured increase in strength might suggest. Possible roles for improved co-ordination and changes in motivation and/or pain tolerance after therapy cannot be excluded. No differences in the clinical outcome were observed between the three therapy groups, and the changes in physical performance after therapy did not correlate with the clinical outcome. It is therefore questionable whether strength measurements have any clinical significance in documenting the success of rehabilitation programmes, other than on a motivational basis. The results of the present study suggest that the value of supervised active therapy programmes does not reside in the reversal of specific muscular deficiencies, but rather in the provision of a source of confirmation/encouragement for the patient, that movement is not harmful, and a foundation upon which to further build. Whether the utilisation of specific training devices, or individual instruction, is necessary to elicit these particular effects is questionable.

Adaptation, Physiological↗
PubMed2025

A robust transfer learning approach for high-dimensional linear regression to support integration of multi-source gene expression data.

Transfer learning aims to integrate useful information from multi-source datasets to improve the learning performance of target data. This can be effectively applied in genomics when we learn the gene associations in a target tissue, and data from other tissues can be integrated. However, heavy-tail distribution and outliers are common in genomics data, which poses challenges to the effectiveness of current transfer learning approaches. In this paper, we study the transfer learning problem under high-dimensional linear models with t-distributed error (Trans-PtLR), which aims to improve the estimation and prediction of target data by borrowing information from useful source data and offering robustness to accommodate complex data with heavy tails and outliers. In the oracle case with known transferable source datasets, a transfer learning algorithm based on penalized maximum likelihood and expectation-maximization algorithm is established. To avoid including non-informative sources, we propose to select the transferable sources based on cross-validation. Extensive simulation experiments as well as an application demonstrate that Trans-PtLR demonstrates robustness and better performance of estimation and prediction when heavy-tail and outliers exist compared to transfer learning for linear regression model with normal error distribution. Data integration, Variable selection, T distribution, Expectation maximization algorithm, Genotype-Tissue Expression, Cross validation.

Linear Models↗
PubMed1997

Integration of radiotherapy planning systems and radiotherapy treatment equipment: 11 years experience.

PURPOSE: We have investigated the requirements, design, implementation, and operation of a computer-controlled medical accelerator with multileaf collimator (MLC), integrated with a radiation treatment-planning system (RTPS), and we report on the performance, benefits, and lessons learned from this experience. METHODS AND MATERIALS: In 1984 the University of Washington installed a computer-controlled radiation therapy machine (the Clinical Neutron Therapy System, or CNTS) with a multileaf collimator. Since the beginning of operation the control system computer has been connected by commercially available network hardware and software to three generations of radiation treatment-planning systems. Semiautomated setup and completely computerized check and confirm were incorporated into the system from the beginning of clinical operation in 1984. The system cannot deliver a patient treatment without a computer-prepared treatment plan. RESULTS: The CNTS has been in use for routine patient treatments for over 11 years. The cost of the network connection and software was an insignificant fraction of the facility cost. Operation has been efficient and reliable. Of the 441 machine-related session reschedulings (out of 18,432 sessions total) during the past 9 years, only 20 were due to problems with data transfer between the RTPS and CNTS, associated primarily with two incidents. Close integration with the treatment-planning system allows complex treatments to be delivered. Dramatic evolution of the departmental treatment-planning system has not required any changes or redesign of either the accelerator control system or the network connection. CONCLUSIONS: Our experience shows that a large degree of automation is possible with reasonable effort, by using well-known software and hardware design strategies. The lessons we have learned from this can be carried over into photon therapy now that photon accelerators with MLC facilities are commercially available.

Computer Communication Networks↗
PubMed2026

Can host genetics transform the sustainable control of tropical theileriosis? Insights from the Tick-Theileria interface.

Tropical theileriosis, caused by the tick-transmitted apicomplexan parasite Theileria annulata, remains a major constraint on cattle production across North Africa, the Mediterranean basin, the Middle East and South Asia. Current control depends on acaricides, the theilericidal drug buparvaquone and live attenuated schizont vaccines, but acaricide resistance, buparvaquone-resistance mutations and the logistical demands of vaccination are eroding the sustainability of these tools. Host genetics offers a complementary and durable alternative. Indigenous Bos indicus breeds are consistently more resistant to ticks and tolerate T. annulata infection better than exotic Bos taurus cattle, and this advantage has a measurable heritable component. Unlike previous reviews, which treat tick resistance, T. annulata immunobiology and livestock genomic selection as separate subjects, we integrate all three and assess host genetics specifically against the failure modes of current control. We review the tick, parasite and host interface, the evidence for natural resistance, and the genetic and immunological mechanisms involved, including signal-regulatory protein, bovine major histocompatibility complex class II and inflammatory pathway genes. We then assess whether genomic selection, multi-omics, machine learning and gene editing can translate these mechanisms into resistant cattle, and we weigh the biological, economic and infrastructural barriers to implementation. The evidence indicates that host genetics will not replace existing control but could reduce reliance on acaricides and chemotherapy. That contribution remains prospective rather than demonstrated: no resistance marker for T. annulata has yet been validated, prediction accuracies are moderate and transfer poorly between breeds, and no endemic production system has implemented selection for resistance.

Animals↗
PubMed2006

Prediction of standard Gibbs energies of the transfer of peptide anions from aqueous solution to nitrobenzene based on support vector machine and the heuristic method.

Quantitative structure-property relationship (QSPR) method was performed for the prediction of the standard Gibbs energies (DeltaGtheta) of the transfer of peptide anions from aqueous solution to nitrobenzene. Descriptors calculated from the molecular structures alone were used to represent the characteristics of the peptides. The four molecular descriptors selected by the heuristic method (HM) in COmprehensive DEscriptors for Structural and Statistical Analysis (CODESSA) were used as inputs for support vector machine (SVM) and radial basis function neural networks (RNFNN). The results obtained by the novel machine learning technique, SVM, were compared with those obtained by HM and RBFNN. The root mean squared errors (RMS) of the training, predicted and overall data sets are 2.192, 2.541 and 2.267 unit (kJ/mol) for HM, 1.604, 2.478 and 1.817 unit (kJ/mol) for RBFNN and 1.5621, 2.364 and 1.756 unit (kJ/mol) for SVM, respectively. The prediction results were in agreement with the experimental values. This paper provided a potential method for predicting the physiochemical property (DeltaGtheta) of various small peptides.

Anions↗
PubMed1995

Evolving mobile robots in simulated and real environments.

The problem of the validity of simulation is particularly relevant for methodologies that use machine learning techniques to develop control systems for autonomous robots, as, for instance, the artificial life approach known as evolutionary robotics. In fact, although it has been demonstrated that training or evolving robots in real environments is possible, the number of trials needed to test the system discourages the use of physical robots during the training period. By evolving neural controllers for a Khepera robot in computer simulations and then transferring the agents obtained to the real environment we show that (a) an accurate model of a particular robot-environment dynamics can be built by sampling the real world through the sensors and the actuators of the robot; (b) the performance gap between the obtained behaviors in simulated and real environments may be significantly reduced by introducing a "conservative" form of noise; (c) if a decrease in performance is observed when the system is transferred to a real environment, successful and robust results can be obtained by continuing the evolutionary process in the real environment for a few generations.

Algorithms↗
PubMed2024

Water source, latrine type, and rainfall are associated with detection of non-optimal and enteric bacteria in the vaginal microbiome: a prospective observational cohort study nested within a cluster randomized controlled trial.

BACKGROUND: Less than one-third of sub-Saharan Africans have access to improved water sources. In US, Indian, and African studies, Bacterial vaginosis (BV) is increased among women with poor water, sanitation, and hygiene (WASH). We examined water source, sanitation (latrine type), and rainfall in relation to the vaginal microbiome (VMB). METHODS: In a cluster randomized controlled trial of menstrual cups and cash transfer, we measured the impact of cups on VMB via 16S rRNA gene amplicon sequencing in a subset of 436 adolescent girls. We analyzed how self-reported water source and latrine type at home related to VMB over 18-months, examining community state type I (CST-I, L. crispatus dominant) vs. other CST; alpha diversity; targeted taxa (coliform and other water-related pathogens); and non-targeted taxa via machine learning approaches. Mixed effects multivariable longitudinal models were adjusted for intervention arm, age, socioeconomic status, sexual activity, and cluster-level school WASH and rainfall (in millimeters). RESULTS: Adjusting for all covariates in all models: (1) the odds of CST-I were increased among participants with piped water (vs. pond), and decreased with traditional pit latrine vs. flush toilet. (2) Alpha diversity varied by water source and latrine type without consistent trends. (3) Coliform bacteria relative abundance (RA) was higher among participants with traditional pit or ventilated improved pit latrines vs. flush toilet, and higher among participants relying on stream vs. pond water. Streptococcus agalactiae RA was higher among participants with non-flush toilets, while Bacteroides fragilis RA was lower with non-flush toilets. (4) Key taxa from non-targeted analyses associated with water source and latrine type included typical vaginal bacteria, opportunistic pathogens, and urinary tract pathobionts. (6) Increased rainfall was associated with decreased odds of CST-I. TRIAL REGISTRATION: ClinicalTrials.gov NCT03051789, February 14, 2017.

Adolescent↗
PubMed2004

The transition from an analog to a digital echocardiography laboratory: the Mayo experience.

Our echocardiography laboratory has completed the transition from an analog to a digital imaging laboratory. The process involved a thorough analysis of the practice and design of a complete digital solution. After an effective implementation of a robust reporting solution, the transition to digital imaging ensued. The implementation plan for digital imaging enabled the transition to be made without adversely affecting the practice, which has an average daily examination load of more than 200 patients. Full-load testing simulations were required on high-end image processing review workstations, high-speed local area network connections to each imaging suite, and new clustered imaging servers. We had to test the ability of approximately 100 ultrasound machines with 3 idiosyncratic ultrasound platforms to allow digital image transfer to the new imaging servers over the local area network. Our sonographer, allied health, and physician staff of more than 250 persons had to be trained to adapt to important changes that the digital echocardiographic image acquisition and review platform would have on their specific roles in the laboratory. The efficiency of the implementation, reporting, and imaging resulted in improved staff resource allocation. The lessons learned may benefit laboratories with a similar interest in making the transition to a digital imaging laboratory with minimal effect on patient scheduling and staff satisfaction. The effect of the transition to a completely digital laboratory on operating expenses, imaging, and reviewing systems and the impact on the clinical practice of echocardiography at our institution are presented, with emphasis on the particular aspects of transitioning to digital imaging.

Cardiology↗
PubMed1996

The cerebellum: a neuronal learning machine?

Comparison of two seemingly quite different behaviors yields a surprisingly consistent picture of the role of the cerebellum in motor learning. Behavioral and physiological data about classical conditioning of the eyelid response and motor learning in the vestibulo-ocular reflex suggests that (i) plasticity is distributed between the cerebellar cortex and the deep cerebellar nuclei; (ii) the cerebellar cortex plays a special role in learning the timing of movement; and (iii) the cerebellar cortex guides learning in the deep nuclei, which may allow learning to be transferred from the cortex to the deep nuclei. Because many of the similarities in the data from the two systems typify general features of cerebellar organization, the cerebellar mechanisms of learning in these two systems may represent principles that apply to many motor systems.

Animals↗
PubMed2026

Crosstalk between cysteine and lysine modifications: Integrating redox and metabolic regulation.

Protein post-translational modifications (PTMs) on amino acid residues enable dynamic cellular responses to changes in metabolic and redox state. Cysteine and lysine are among the most extensively modified amino acid residues, with both undergoing a diversity of acylation and oxidative modifications. Indeed, proximal (<10&#x202f;&#xc5;) cysteine and lysine residues may form integration nodes for crosstalk between metabolism and redox homeostasis pathways. This review highlights the interaction of proximal Cys-Lys residues, including influence on residue pKa by local electrostatics, cysteine-to-lysine transfer of PTM moieties, and covalent crosslinking. We discuss candidate Cys-Lys regulatory pairs in proteins involved in redox regulation, proteostasis, metabolic adaptation and inflammation. We further utilize computational modeling to identify proximity between cysteine and lysine residues in proteins known to be regulated by acylation and oxidative PTMs, and to demonstrate changes in these distances and local electrostatic potential due to lysine acetylation. Finally, we review how mass spectrometry-based proteomics and machine-learning PTM predictive tools can enable the identification, validation, and interpretation of proximal Cys-Lys interactions that regulate cellular responses to oxidative challenge and metabolic flux.

Cysteine↗
PubMed2025

Deep learning-based annotation of plant abiotic stress resistance genes for crops.

The declining costs of DNA sequencing have expanded genomic data, crucial for understanding plant abiotic stress responses and crop improvement. However, accurate gene annotation remains challenging. To address this limitation, we propose the PASRGA, a deep learning approach that leverages transfer learning and contrastive learning to annotate genes related to drought, salt, cold, and UV resistance. PASRGA achieves high F1-scores, area under the receiver operating characteristic (AUROC), area under the precision-recall curve (AUPRC), and Matthews correlation coefficient (MCC) in annotating stress resistance genes, significantly outperforming the general protein annotation model CLEAN, the plant phosphatase gene annotation model PF-NET, the top-ranked model in the CAFA5 challenge NetGO 4.0, and four traditional machine learning methods. Its effectiveness was further validated with a salt stress treatment experiment in Eutrema salsugineum. To facilitate crop breeding practices, we utilized PASRGA to annotate the genomes of 17 major crops. To improve accessibility and utility, we incorporated both manually curated and PASRGA-predicted gene data, together with the PASRGA tool, into the PlantASRG database (https://bioinfor.nefu.edu.cn/PlantASRG/). This comprehensive resource aims to support crop breeding initiatives and ensure food security.

Crops, Agricultural↗
PubMedDate not supplied

Electronic van der Waals surface property descriptors and genetic algorithms for developing structure-activity correlations in olfactory databases.

A methodology to facilitate the intelligent design of new odorants (e.g., musks) with specialized properties has been developed as part of an ongoing research effort in machine learning. In a traditional framework, the introduction of a new odorant is a lengthy, costly, and laborious discovery, development, and testing process. We propose to streamline this process utilizing large existing olfactory databases available through the open scientific literature as input for a new structure/activity correlation methodology. The first step in this process is to characterize each molecule in the database by an appropriate set of descriptors. To accomplish this task, an enhanced version of Breneman's Transferable Atom Equivalent (TAE) descriptor methodology will be used to create a large set of electron density derived shape/property hybrid (PEST), wavelet coefficient (WCD), and TAE histogram descriptors. We have chosen these molecular property descriptors to represent the problem because they have been shown to contain pertinent shape and electronic properties of the molecule and correlate with key modes of intermolecular interactions. Traditional QSAR methodologies, which employ fragment based descriptors, have been shown to be effective for QSAR development within homologous sets of molecules but are less effective when applied to data sets containing a great deal of structural variation. In contrast to previous attempts at SAR, our use of shape-aware electron density based molecular property descriptors has removed many of the limitations brought about by the use of descriptors based on substructure fragments, molecular surface properties, or other whole molecule descriptors. Another reason for the mixed success of past QSAR efforts can be traced to the nature of the underlying modeling problem, which is often quite complex. To meet these challenges, a genetic algorithm for pattern recognition analysis has been developed that selects descriptors which create class separation in a plot of the two largest principal components of the data while simultaneously searching for features that increase clustering of the data.

Journal Article↗
PubMed2006

A vertical-energy-thresholding procedure for data reduction with multiple complex curves.

Due to the development of sensing and computer technology, measurements of many process variables are available in current manufacturing processes. It is very challenging, however, to process a large amount of information in a limited time in order to make decisions about the health of the processes and products. This paper develops a "preprocessing" procedure for multiple sets of complicated functional data in order to reduce the data size for supporting timely decision analyses. The data type studied has been used for fault detection, root-cause analysis, and quality improvement in such engineering applications as automobile and semiconductor manufacturing and nanomachining processes. The proposed vertical-energy-thresholding (VET) procedure balances the reconstruction error against data-reduction efficiency so that it is effective in capturing key patterns in the multiple data signals. The selected wavelet coefficients are treated as the "reduced-size" data in subsequent analyses for decision making. This enhances the ability of the existing statistical and machine-learning procedures to handle high-dimensional functional data. A few real-life examples demonstrate the effectiveness of our proposed procedure compared to several ad hoc techniques extended from single-curve-based data modeling and denoising procedures.

Algorithms↗
PubMed2005

A review on the integration of artificial intelligence into coastal modeling.

With the development of computing technology, mechanistic models are often employed to simulate processes in coastal environments. However, these predictive tools are inevitably highly specialized, involving certain assumptions and/or limitations, and can be manipulated only by experienced engineers who have a thorough understanding of the underlying theories. This results in significant constraints on their manipulation as well as large gaps in understanding and expectations between the developers and practitioners of a model. The recent advancements in artificial intelligence (AI) technologies are making it possible to integrate machine learning capabilities into numerical modeling systems in order to bridge the gaps and lessen the demands on human experts. The objective of this paper is to review the state-of-the-art in the integration of different AI technologies into coastal modeling. The algorithms and methods studied include knowledge-based systems, genetic algorithms, artificial neural networks, and fuzzy inference systems. More focus is given to knowledge-based systems, which have apparent advantages over the others in allowing more transparent transfers of knowledge in the use of models and in furnishing the intelligent manipulation of calibration parameters. Of course, the other AI methods also have their individual contributions towards accurate and reliable predictions of coastal processes. The integrated model might be very powerful, since the advantages of each technique can be combined.

Artificial Intelligence↗
PubMed2003

Behavioural development in a matching-to-sample task and token use by an infant chimpanzee reared by his mother.

We investigated the behavioural and cognitive development of a captive male infant chimpanzee, Ayumu, raised by his mother, Ai. Here we report Ayumu's achievements up to the age of 2 years and 3 months, in the context of complex computer-controlled tasks. From soon after birth, Ayumu had been present during an experiment performed by his mother. The task consisted of two phases, a matching-to-sample task in which she received token rewards, and the insertion of these tokens into a vending machine to obtain food rewards. Ayumu himself received no reward or encouragement from humans for any of the actions he exhibited during the experiment. At the age of 9 months and 3 weeks, Ayumu performed his first matching-to-sample trial. At around 1 year and 3 months, he began to perform them consistently. Also during this period, he frequently stole food rewards from his mother. At 2 years and 3 months, Ayumu succeeded for the first time in inserting a token into the vending machine. Once he had succeeded in using a token, he performed both phases of the task in sequence 20 times consecutively. The infant's behaviour was not shaped by food rewards but by a strong motivation to copy his mother's behaviour. Our observations of Ayumu thus mirror the learning processes shown by wild chimpanzees.

Animals↗
PubMed2006

Protein ranking by semi-supervised network propagation.

BACKGROUND: Biologists regularly search DNA or protein databases for sequences that share an evolutionary or functional relationship with a given query sequence. Traditional search methods, such as BLAST and PSI-BLAST, focus on detecting statistically significant pairwise sequence alignments and often miss more subtle sequence similarity. Recent work in the machine learning community has shown that exploiting the global structure of the network defined by these pairwise similarities can help detect more remote relationships than a purely local measure. METHODS: We review RankProp, a ranking algorithm that exploits the global network structure of similarity relationships among proteins in a database by performing a diffusion operation on a protein similarity network with weighted edges. The original RankProp algorithm is unsupervised. Here, we describe a semi-supervised version of the algorithm that uses labeled examples. Three possible ways of incorporating label information are considered: (i) as a validation set for model selection, (ii) to learn a new network, by choosing which transfer function to use for a given query, and (iii) to estimate edge weights, which measure the probability of inferring structural similarity. RESULTS: Benchmarked on a human-curated database of protein structures, the original RankProp algorithm provides significant improvement over local network search algorithms such as PSI-BLAST. Furthermore, we show here that labeled data can be used to learn a network without any need for estimating parameters of the transfer function, and that diffusion on this learned network produces better results than the original RankProp algorithm with a fixed network. CONCLUSION: In order to gain maximal information from a network, labeled and unlabeled data should be used to extract both local and global structure.

Algorithms↗
PubMed2006

Monitoring in the field.

This review looks at the challenges faced when monitoring patients in the field environment. It is considered from the perspective of the UK Defence Medical Services and their experiences over the past 20 yr. The lessons learned are applicable to many other circumstances where a high standard of care, for a large spectrum of patients, is to be delivered in the most inhospitable conditions. The environmental influences on monitoring equipment such as extremes of heat, cold and altitude must be considered and dealt with. Minimal monitoring standards required by professional bodies have to be undertaken, but there is a need to exceed them to compensate for the untoward effects of hostile environments. Patient and machine variables monitored and their relative importance in the field are also explored. Varying field locations are illustrated and the types of monitoring required to care for patients in different areas of field units are discussed. Patient transfers and the particular difficulties encountered in the military context are also reviewed. Undertaking aeromedical evacuation is one of the most challenging environments in the field and the solutions required to undertake it are explored. These considerations are used to propose design requirements necessary to provide appropriate monitoring in all other field conditions. The standards set for carriage of equipment in the air and the testing required allowing compliance with the regulations in force in the UK, are outlined. Finally the importance of practitioner training to undertake these roles in the field is discussed.

Air Ambulances↗