Early warning signals have limited applicability to empirical lake data.

Research aimed at identifying indicators of persistent abrupt shifts in ecological communities, a.k.a regime shifts, has led to the development of a suite of early warning signals (EWSs). As these often perform inaccurately when applied to real-world observational data, it remains unclear whether critical transitions are the dominant mechanism of regime shifts and, if so, which EWS methods can predict them. Here, using multi-trophic planktonic data on multiple lakes from around the world, we classify both lake dynamics and the reliability of classic and second generation EWSs methods to predict whole-ecosystem change. We find few instances of critical transitions, with different trophic levels often expressing different forms of abrupt change. The ability to predict this change is highly processing dependant, with most indicators not performing better than chance, multivariate EWSs being weakly superior to univariate, and a recent machine learning model performing poorly. Our results suggest that predictive ecology should start to move away from the concept of critical transitions, developing methods suitable for predicting resilience loss not limited to the strict bounds of bifurcation theory.

Evading tipping points in socio-mutualistic networks via structure mediated optimal strategy.

The threat of large-scale pollinator decline is increasing globally under stress from multiple anthropogenic pressures. Traditional approaches have focused on managing endangered species at an individual level, in which the effect of complex interactions such as mutualism and competition are amiss. Here, we develop a coupled socio-mutualistic network model that captures the change in pollinator dynamics with human conservation opinion in a deteriorating environment. We show that the application of social norm (or conservation) at the pollinator nodes is fit to prevent sudden community collapse in representative networks of varied topology. Whilst primitive strategies have focused on regulating abundance as a mitigation strategy, the role of network structure has been largely overlooked. Here, we develop a novel network structure-mediated conservation strategy to find the optimal set of nodes on which norm implementation successfully prevents community collapse. We find that networks of intermediate nestedness require conservation at a minimum number of nodes to prevent a community collapse. We claim the robustness of the optimal conservation strategy (OCS) after validation on several simulated and empirical networks of varied complexity against a broad range of system parameters. Dynamical analysis of the reduced model shows that incorporating social norms allows the pollinator abundance to grow that would have otherwise crossed a tipping point and undergo extinction. Together, this novel means OCS provides a potential plan of action for conserving plant-pollinator networks bridging the gap between research in mutualistic networks and conservation ecology.

Rising temperature drives tipping points in mutualistic networks.

The effect of climate warming on species' physiological parameters, including growth rate, mortality rate and handling time, is well established from empirical data. However, with an alarming rise in global temperature more than ever, predicting the interactive influence of these changes on mutualistic communities remains uncertain. Using 139 real plant-pollinator networks sampled across the globe and a modelling approach, we study the impact of species' individual thermal responses on mutualistic communities. We show that at low mutualistic strength plant-pollinator networks are at potential risk of rapid transitions at higher temperatures. Evidently, generalist species play a critical role in guiding tipping points in mutualistic networks. Further, we derive stability criteria for the networks in a range of temperatures using a two-dimensional reduced model. We identify network structures that can ascertain the delay of a community collapse. Until the end of this century, on account of increasing climate warming many real mutualistic networks are likely to be under the threat of sudden collapse, and we frame strategies to mitigate this. Together, our results indicate that knowing individual species' thermal responses and network structure can improve predictions for communities facing rapid transitions.

Tipping points in spatial ecosystems driven by short-range correlated noise.

Complex spatial systems can experience critical transitions or tippings on crossing a threshold value in their response to stochastic perturbations. While previous studies have well characterized the impact of white noise on tipping, the effect of correlated noise in spatial ecosystems remains largely unexplored. Here, we investigate the effect of both multiplicative and additive Ornstein-Uhlenbeck (OU) correlated noise on the occurrence of critical transitions in spatial ecosystems. We find that decreasing the noise correlation time of OU (exponentially correlated) noise aggravates the chance of critical transitions in spatiotemporal ecological systems. Our results hold good and are supported by the analysis of three well-studied spatial ecological models of varying nonlinearity. We also compute spatial early warning indicators (e.g., spatial variance, spatial skewness, and spatial correlation) to determine their reliability in anticipating tipping points with variations in noise correlation. The indicators of critical transitions exhibit mixed success in forewarning the occurrence of a tipping point, as indicated by the distribution of Kendall's rank correlation.

Identifying critical transitions in complex diseases.

Mortality and the burden of diseases worldwide continue to reach substantial numbers with societal development and urbanization. In the face of decline in human health, early detection of complex diseases is indispensable, albeit challenging. In this review, we document the research carried out thus far on the appearance of complex diseases marked by a critical transition or a sudden shift from a healthy state to a disease state. The theory of resilience and critical slowing down can provide practical tools to forecast the onset of various fatal and perpetuating diseases. However, critical transitions in diseases across diverse temporal and spatial scales may not always be preceded by critical slowing down. In this backdrop, an in-depth study of the underlying molecular mechanisms provides dynamic network biomarkers that can forecast potential critical transitions. We have put together the theory of complex diseases and resilience, and have discussed the need for advanced research in developing early warning signals in the field of medicine and health care. We conclude the review with a few open questions and prospects for research in this emerging field.

Machine learning methods trained on simple models can predict critical transitions in complex natural systems.

Forecasting sudden changes in complex systems is a critical but challenging task, with previously developed methods varying widely in their reliability. Here we develop a novel detection method, using simple theoretical models to train a deep neural network to detect critical transitions-the Early Warning Signal Network (EWSNet). We then demonstrate that this network, trained on simulated data, can reliably predict observed real-world transitions in systems ranging from rapid climatic change to the collapse of ecological populations. Importantly, our model appears to capture latent properties in time series missed by previous warning signals approaches, allowing us to not only detect if a transition is approaching, but critically whether the collapse will be catastrophic or non-catastrophic. These novel properties mean EWSNet has the potential to serve as an indicator of transitions across a broad spectrum of complex systems, without requiring information on the structure of the system being monitored. Our work highlights the practicality of deep learning for addressing further questions pertaining to ecosystem collapse and has much broader management implications.

Computed tomography guided lung biopsy under general anaesthesia with apnoea: Preliminary experience.

Certain pulmonary lesions may be challenging to biopsy with conventional computed tomography percutaneous lung biopsy (CTPLB) under local anaesthesia (LA) which requires consistent patient breath holding to minimise complications. We aim to describe and evaluate the feasibility of CTPLB under general anaesthesia (GA) with apnoea, comparing results to patients undergoing biopsy under LA. This was a retrospective analysis of CTPLB with 18 GA and 137 LA patients. All biopsies were performed using a co-axial needle system in the radiology department on a multi-detector CT scanner with patient positioning determined by assessing shortest distance to target lesion. GA cases were performed under relaxant anaesthesia with intermittent positive pressure ventilation. Lower lobar lesion location and a combination of size and location (including proximity to critical structures) were indications for GA biopsy in >90% of patients. Mean lesion size for GA biopsies was 18 mm and control group 30 mm (P < 0.006) and mean pleura to lesion distance 29 and 11 mm, respectively (P < 0.0009). Pneumothorax rates were lower in our GA biopsy group (11%) compared to control group (42%) (P < 0.05). No anaesthetic complications were encountered. All GA samples were diagnostic. Based on a small number of patients, CTPLB under GA with apnoea seems a safe, feasible alternative to conventional CTPLB under LA for technically challenging lesions. This technique is routinely employed at our centre allowing access to lesions previously deemed unsafe to biopsy.

Imaging characteristics of nodal metastases in paraganglioma, ameloblastoma and olfactory neuroblastoma: case reports and literature review.

Paraganglioma, ameloblastoma and olfactory neuroblastoma are uncommon primary head and neck tumours. When nodal metastases from these tumours occur, they may present later than and with different imaging characteristics compared with squamous cell carcinoma (SCC), demonstrating appearances similar to the primary tumour type rather than features typical of metastatic nodal SCC. We present three cases in which imaging characteristics of nodal metastases in paraganglioma, ameloblastoma and olfactory neuroblastoma mimicked the primary tumour and discuss their implications for clinicoradiological follow-up.

The effect of formal training on the clinical utility of transcranial Doppler ultrasound monitoring in patients with aneurysmal subarachnoid haemorrhage.

We have previously shown that the clinical utility of transcranial Doppler (TCD) ultrasound monitoring for vasospasm in patients with aneurysmal subarachnoid haemorrhage, as performed by untrained operators in a busy neurosurgical unit, is questionable, despite the recommendations for its use in the literature. We determined if formal training improved the utility of TCD. Twelve untrained operators and one trained operator performed a total of 206 TCD examinations. There was poor agreement of results between trained and untrained operators. For the left middle cerebral artery (MCA), right MCA, left anterior cerebral artery (ACA) and right ACA, the blood flow velocities (BFV) recorded by the trained operator were greater than those recorded by the untrained operators by a mean (95% confidence interval) of 27.7 (25.0-30.4), 24.3 (21.4-27.1), 28.2 (25.6-30.9) and 28.1 (24.9-31.1) cm/s, respectively (p<0.001 for all vessels). Greater sensitivity was observed in TCD measurements from the trained operator (100%) compared to untrained operators (40%). To improve the utility of TCD, operators should be provided with training or a professional sonographer employed.

The role of transcranial Doppler ultrasound monitoring in patients with aneurysmal subarachnoid haemorrhage.

The effect of transcranial Doppler (TCD) ultrasound monitoring of vasospasm on patient management following aneurysmal subarachnoid haemorrhage (aSAH) remains unclear. We reviewed our departmental use of TCD by retrospectively analysing 152 medical records. Results of investigations and management changes, including frequency of neurological monitoring and changes in triple H therapy, were examined. TCD monitoring occurred in 87 patients (57%) by untrained neurosurgical registrars. There was high variability in the number of operators for each patient (over 50% of patients had more than two different operators), insonation protocol and monitoring duration (at least 50% of patients were monitored for fewer than seven days). TCD results influenced management in only 18 (12%) patients, while clinical deterioration or improvement dictated more than 80% of changes in triple H therapy and neurological monitoring. Prospective validation in similar neurosurgical settings is needed to justify continued usage of TCD monitoring. Formal training for operators and a standard monitoring protocol should also be considered to increase TCD utility. Prospective evaluation of TCD at our centre has recently been completed.