Antibiotic pharmacokinetics in infected pleural effusions.

Pleural infection is usually treated with empirical broad-spectrum antibiotics, but limited data exist on their penetrance into the infected pleural space. We performed a pharmacokinetic study analysing the concentration of five intravenous antibiotics across 146 separate time points in 35 patients (amoxicillin, metronidazole, piperacillin-tazobactam, clindamycin and cotrimoxazole). All antibiotics tested, apart from co-trimoxazole, reach pleural fluid levels equivalent to levels within the blood and well above the relevant minimum inhibitory concentrations. The results demonstrate that concerns about the penetration of commonly used antibiotics, apart from co-trimoxazole, into the infected pleural space are unfounded.

Grazing livestock move by Lévy walks: Implications for soil health and environment.

Grazing livestock plays an important role in the context of food security, agricultural sustainability and climate change. Understanding how livestock move and interact with their environment may offer new insights on how grazing practices impact soil and ecosystem functions at spatial and temporal scales where knowledge is currently limited. We characterized daily and seasonal grazing patterns using Global Positioning System (GPS) data from two grazing strategies: conventionally- and rotationally-grazed pastures. Livestock movement was consistent with the so-called Lévy walks, and could thus be simulated with Lévy-walk based probability density functions. Our newly introduced "Moovement model" links grazing patterns with soil structure and related functions by coupling animal movement and soil structure dynamics models, allowing to predict spatially-explicit changes in key soil properties. Predicted post-grazing management-specific bulk densities were consistent with field measurements and confirmed that rotational grazing produced similar disturbance as conventional grazing despite hosting higher stock densities. Harnessing information on livestock movement and its impacts in soil structure within a modelling framework can help testing and optimizing grazing strategies for ameliorating their impact on soil health and environment.

Sustained pupil responses are modulated by predictability of auditory sequences.

The brain is highly sensitive to auditory regularities and exploits the predictable order of sounds in many situations, from parsing complex auditory scenes, to the acquisition of language. To understand the impact of stimulus predictability on perception, it is important to determine how the detection of predictable structure influences processing and attention. Here we use pupillometry to gain insight into the effect of sensory regularity on arousal. Pupillometry is a commonly used measure of salience and processing effort, with more perceptually salient or perceptually demanding stimuli consistently associated with larger pupil diameters.In two experiments we tracked human listeners' pupil dynamics while they listened to sequences of 50ms tone pips of different frequencies. The order of the tone pips was either random, contained deterministic (fully predictable) regularities (experiment 1, n = 18, 11 female) or had a probabilistic regularity structure (experiment 2, n = 20, 17 female). The sequences were rapid, preventing conscious tracking of sequence structure thus allowing us to focus on the automatic extraction of different types of regularities. We hypothesized that if regularity facilitates processing by reducing processing demands, a smaller pupil diameter would be seen in response to regular relative to random patterns. Conversely, if regularity is associated with heightened arousal and attention (i.e. engages processing resources) the opposite pattern would be expected. In both experiments we observed a smaller sustained (tonic) pupil diameter for regular compared with random sequences, consistent with the former hypothesis and confirming that predictability facilitates sequence processing.SIGNIFICANCE STATEMENTThe brain is highly sensitive to auditory regularities. To appreciate the impact that the presence of predictability has on perception, we need to better understand how a predictable structure influences processing and attention. We recorded listeners' pupil responses to sequences of tones that followed either a predictable or unpredictable pattern, as the pupil can be used to implicitly tap into these different cognitive processes. We found that the pupil showed a smaller sustained diameter to predictable sequences, indicating that predictability eased processing rather than boosted attention. The findings suggest that the pupil response can be used to study the automatic extraction of regularities, and that the effects are most consistent with predictability helping the listener to efficiently process upcoming sounds.

Modelling cassava production and pest management under biotic and abiotic constraints.

KEY MESSAGE: We summarise modelling studies of the most economically important cassava diseases and arthropods, highlighting research gaps where modelling can contribute to the better management of these in the areas of surveillance, control, and host-pest dynamics understanding the effects of climate change and future challenges in modelling. For over 30 years, experimental and theoretical studies have sought to better understand the epidemiology of cassava diseases and arthropods that affect production and lead to considerable yield loss, to detect and control them more effectively. In this review, we consider the contribution of modelling studies to that understanding. We summarise studies of the most economically important cassava pests, including cassava mosaic disease, cassava brown streak disease, the cassava mealybug, and the cassava green mite. We focus on conceptual models of system dynamics rather than statistical methods. Through our analysis we identified areas where modelling has contributed and areas where modelling can improve and further contribute. Firstly, we identify research challenges in the modelling developed for the surveillance, detection and control of cassava pests, and propose approaches to overcome these. We then look at the contributions that modelling has accomplished in the understanding of the interaction and dynamics of cassava and its' pests, highlighting success stories and areas where improvement is needed. Thirdly, we look at the possibility that novel modelling applications can achieve to provide insights into the impacts and uncertainties of climate change. Finally, we identify research gaps, challenges, and opportunities where modelling can develop and contribute for the management of cassava pests, highlighting the recent advances in understanding molecular mechanisms of plant defence.

A feasibility randomised trial comparing therapeutic thoracentesis to chest tube insertion for the management of pleural infection: results from the ACTion trial.

BACKGROUND: Pleural infection is a complex condition with a considerable healthcare burden. The average hospital stay for pleural infection is 14 days. Current standard of care defaults to chest tube insertion and intravenous antibiotics. There have been no randomised trials on the use of therapeutic thoracentesis (TT) for pleural fluid drainage in pleural infection. AIMS AND OBJECTIVES: To assess the feasibility of a full-scale trial of chest tube vs TT for pleural infection in a single UK centre. The primary outcome was defined as the acceptability of randomisation to patients. METHODS: Adult patients admitted with a pleural effusion felt to be related to infection and meeting criteria for drainage (based on international guidelines) were eligible for randomisation. Participants were randomised (1:1) to chest tube insertion or TT with daily review assessing need for further drainages or other therapies. Neither participant nor clinician were blinded to treatment allocation. Patients were followed up at 90 days post-randomisation. RESULTS: From September 2019 to June 2021, 51 patients were diagnosed with pleural infection (complex parapneumonic effusion/empyema). Eleven patients met the inclusion criteria for trial and 10 patients were randomised (91%). The COVID-19 pandemic had a substantial impact on recruitment. Data completeness was high in both groups with no protocol deviations. Patients randomised to TT had a significantly shorter overall mean hospital stay (5.4 days, SD 5.1) compared to the chest tube control group (13 days, SD 6.0), p = 0.04. Total number of pleural procedures required per patient were similar, 1.2 in chest tube group and 1.4 in TT group. No patient required a surgical referral. Adverse events were similar between the groups with no readmissions related to pleural infection. CONCLUSIONS: The ACTion trial met its pre-specified feasibility criteria for patient acceptability but other issues around feasibility of a full-scale trial remain. From the results available the hypothesis that TT can reduce length of stay in pleural infection should be explored further. TRIAL REGISTRATION: ISRCTN: 84674413.

Patient outcomes after hospitalisation with COVID-19 and implications for follow-up: results from a prospective UK cohort.

The longer-term consequences of SARS-CoV-2 infection are uncertain. Consecutive patients hospitalised with COVID-19 were prospectively recruited to this observational study (n=163). At 8-12 weeks postadmission, survivors were invited to a systematic clinical follow-up. Of 131 participants, 110 attended the follow-up clinic. Most (74%) had persistent symptoms (notably breathlessness and excessive fatigue) and limitations in reported physical ability. However, clinically significant abnormalities in chest radiograph, exercise tests, blood tests and spirometry were less frequent (35%), especially in patients not requiring supplementary oxygen during their acute infection (7%). Results suggest that a holistic approach focusing on rehabilitation and general well-being is paramount.

What makes or breaks a campaign to stop an invading plant pathogen?

Diseases in humans, animals and plants remain an important challenge in our society. Effective control of invasive pathogens often requires coordinated concerted action of a large group of stakeholders. Both epidemiological and human behavioural factors influence the outcome of a disease control campaign. In mathematical models that are frequently used to guide such campaigns, human behaviour is often ill-represented, if at all. Existing models of human, animal and plant disease that do incorporate participation or compliance are often driven by pay-offs or direct observations of the disease state. It is however very well known that opinion is an important driving factor of human decision making. Here we consider the case study of Citrus Huanglongbing disease (HLB), which is an acute bacterial disease that threatens the sustainability of citrus production across the world. We show how by coupling an epidemiological model of this invasive disease with an opinion dynamics model we are able to answer the question: What makes or breaks the effectiveness of a disease control campaign? Frequent contact between stakeholders and advisors is shown to increase the probability of successful control. More surprisingly, we show that informing stakeholders about the effectiveness of control methods is of much greater importance than prematurely increasing their perceptions of the risk of infection. We discuss the overarching consequences of this finding and the effect on human as well as plant disease epidemics.

An online headphone screening test based on dichotic pitch.

Online experimental platforms can be used as an alternative to, or complement, lab-based research. However, when conducting auditory experiments via online methods, the researcher has limited control over the participants' listening environment. We offer a new method to probe one aspect of that environment, headphone use. Headphones not only provide better control of sound presentation but can also "shield" the listener from background noise. Here we present a rapid (< 3 min) headphone screening test based on Huggins Pitch (HP), a perceptual phenomenon that can only be detected when stimuli are presented dichotically. We validate this test using a cohort of "Trusted" online participants who completed the test using both headphones and loudspeakers. The same participants were also used to test an existing headphone test (AP test; Woods et al., 2017, Attention Perception Psychophysics). We demonstrate that compared to the AP test, the HP test has a higher selectivity for headphone users, rendering it as a compelling alternative to existing methods. Overall, the new HP test correctly detects 80% of headphone users and has a false-positive rate of 20%. Moreover, we demonstrate that combining the HP test with an additional test-either the AP test or an alternative based on a beat test (BT)-can lower the false-positive rate to ~ 7%. This should be useful in situations where headphone use is particularly critical (e.g., dichotic or spatial manipulations). Code for implementing the new tests is publicly available in JavaScript and through Gorilla (gorilla.sc).

Model-based optimisation of agricultural profitability and nutrient management: a practical approach for dealing with issues of scale.

To manage agricultural landscapes more sustainably, we must understand and quantify the synergies and trade-offs between environmental impact, production, and other ecosystem services. Models play an important role in this type of analysis as generally it is infeasible to test multiple scenarios by experiment. These models can be linked with algorithms that optimise for multiple objectives by searching a space of allowable management interventions (the control variables). Optimisation of landscapes for multiple objectives can be computationally challenging, however, particularly if the scale of management is typically smaller (e.g. field scale) than the scale at which the objective is quantified (landscape scale) resulting in a large number of control variables whose impacts do not necessarily scale linearly. In this paper, we explore some practical solutions to this problem through a case study. In our case study, we link a relatively detailed, agricultural landscape model with a multiple-objective optimisation algorithm to determine solutions that both maximise profitability and minimise greenhouse gas emissions in response to management. The optimisation algorithm combines a non-dominated sorting routine with differential evolution, whereby a 'population' of 100 solutions evolves over time to a Pareto optimal front. We show the advantages of using a hierarchical approach to the optimisation, whereby it is applied to finer-scale units first (i.e. fields), and then the solutions from each optimisation are combined in a second step to produce landscape-scale outcomes. We show that if there is no interaction between units, then the solution derived using such an approach will be the same as the one obtained if the landscape is optimised in one step. However, if there is spatial interaction, or if there are constraints on the allowable sets of solutions, then outcomes can be quite different. In these cases, other approaches to increase the efficiency of the optimisation may be more appropriate-such as initialising the control variables for half of the population of solutions with values expected to be near optimal. Our analysis shows the importance of aligning a policy or management recommendation with the appropriate scale.

Accounting for data sparsity when forming spatially coherent zones.

Efficient farm management can be aided by the identification of zones in the landscape. These zones can be informed from different measured variables by ensuring a sense of spatial coherence. Forming spatially coherent zones is an established method in the literature, but has been found to perform poorly when data are sparse. In this paper, we describe the different types of data sparsity and investigate how this impacts the performance of established methods. We introduce a set of methodological advances that address these shortcomings to provide a method for forming spatially coherent zones under data sparsity.

The Effect of Farmers' Decisions on Pest Control with Bt Crops: A Billion Dollar Game of Strategy.

A farmer's decision on whether to control a pest is usually based on the perceived threat of the pest locally and the guidance of commercial advisors. Therefore, farmers in a region are often influenced by similar circumstances, and this can create a coordinated response for pest control that is effective at a landscape scale. This coordinated response is not intentional, but is an emergent property of the system. We propose a framework for understanding the intrinsic feedback mechanisms between the actions of humans and the dynamics of pest populations and demonstrate this framework using the European corn borer, a serious pest in maize crops. We link a model of the European corn borer and a parasite in a landscape with a model that simulates the decisions of individual farmers on what type of maize to grow. Farmers chose whether to grow Bt-maize, which is toxic to the corn borer, or conventional maize for which the seed is cheaper. The problem is akin to the snow-drift problem in game theory; that is to say, if enough farmers choose to grow Bt maize then because the pest is suppressed an individual may benefit from growing conventional maize. We show that the communication network between farmers' and their perceptions of profit and loss affects landscape scale patterns in pest dynamics. We found that although adoption of Bt maize often brings increased financial returns, these rewards oscillate in response to the prevalence of pests.

Communicating the uncertainty in estimated greenhouse gas emissions from agriculture.

In an effort to mitigate anthropogenic effects on the global climate system, industrialised countries are required to quantify and report, for various economic sectors, the annual emissions of greenhouse gases from their several sources and the absorption of the same in different sinks. These estimates are uncertain, and this uncertainty must be communicated effectively, if government bodies, research scientists or members of the public are to draw sound conclusions. Our interest is in communicating the uncertainty in estimates of greenhouse gas emissions from agriculture to those who might directly use the results from the inventory. We tested six methods of communication. These were: a verbal scale using the IPCC calibrated phrases such as 'likely' and 'very unlikely'; probabilities that emissions are within a defined range of values; confidence intervals for the expected value; histograms; box plots; and shaded arrays that depict the probability density of the uncertain quantity. In a formal trial we used these methods to communicate uncertainty about four specific inferences about greenhouse gas emissions in the UK. Sixty four individuals who use results from the greenhouse gas inventory professionally participated in the trial, and we tested how effectively the uncertainty about these inferences was communicated by means of a questionnaire. Our results showed differences in the efficacy of the methods of communication, and interactions with the nature of the target audience. We found that, although the verbal scale was thought to be a good method of communication it did not convey enough information and was open to misinterpretation. Shaded arrays were similarly criticised for being open to misinterpretation, but proved to give the best impression of uncertainty when participants were asked to interpret results from the greenhouse gas inventory. Box plots were most favoured by our participants largely because they were particularly favoured by those who worked in research or had a stronger mathematical background. We propose a combination of methods should be used to convey uncertainty in emissions and that this combination should be tailored to the professional group.

Auditory artificial grammar learning in macaque and marmoset monkeys.

Artificial grammars (AG) are designed to emulate aspects of the structure of language, and AG learning (AGL) paradigms can be used to study the extent of nonhuman animals' structure-learning capabilities. However, different AG structures have been used with nonhuman animals and are difficult to compare across studies and species. We developed a simple quantitative parameter space, which we used to summarize previous nonhuman animal AGL results. This was used to highlight an under-studied AG with a forward-branching structure, designed to model certain aspects of the nondeterministic nature of word transitions in natural language and animal song. We tested whether two monkey species could learn aspects of this auditory AG. After habituating the monkeys to the AG, analysis of video recordings showed that common marmosets (New World monkeys) differentiated between well formed, correct testing sequences and those violating the AG structure based primarily on simple learning strategies. By comparison, Rhesus macaques (Old World monkeys) showed evidence for deeper levels of AGL. A novel eye-tracking approach confirmed this result in the macaques and demonstrated evidence for more complex AGL. This study provides evidence for a previously unknown level of AGL complexity in Old World monkeys that seems less evident in New World monkeys, which are more distant evolutionary relatives to humans. The findings allow for the development of both marmosets and macaques as neurobiological model systems to study different aspects of AGL at the neuronal level.

Alterations in platelet proteome signature and impaired platelet integrin αIIbß3 activation in patients with COVID-19.

BACKGROUND: Patients with COVID-19 are at increased risk of thrombosis, which is associated with altered platelet function and coagulopathy, contributing to excess mortality. OBJECTIVES: To characterize the mechanism of altered platelet function in COVID-19 patients. METHODS: The platelet proteome, platelet functional responses, and platelet-neutrophil aggregates were compared between patients hospitalized with COVID-19 and healthy control subjects using tandem mass tag proteomic analysis, Western blotting, and flow cytometry. RESULTS: COVID-19 patients showed a different profile of platelet protein expression (858 altered of the 5773 quantified). Levels of COVID-19 plasma markers were enhanced in the platelets of COVID-19 patients. Gene ontology pathway analysis demonstrated that the levels of granule secretory proteins were raised, whereas those of platelet activation proteins, such as the thrombopoietin receptor and protein kinase Cα, were lowered. Basally, platelets of COVID-19 patients showed enhanced phosphatidylserine exposure, with unaltered integrin αIIbß3 activation and P-selectin expression. Agonist-stimulated integrin αIIbß3 activation and phosphatidylserine exposure, but not P-selectin expression, were decreased in COVID-19 patients. COVID-19 patients had high levels of platelet-neutrophil aggregates, even under basal conditions, compared to controls. This association was disrupted by blocking P-selectin, demonstrating that platelet P-selectin is critical for the interaction. CONCLUSIONS: Overall, our data suggest the presence of 2 platelet populations in patients with COVID-19: one of circulating platelets with an altered proteome and reduced functional responses and another of P-selectin-expressing neutrophil-associated platelets. Platelet-driven thromboinflammation may therefore be one of the key factors enhancing the risk of thrombosis in COVID-19 patients.

Prolonged T-cell activation and long COVID symptoms independently associate with severe COVID-19 at 3 months.

Coronavirus disease-19 (COVID-19) causes immune perturbations which may persist long term, and patients frequently report ongoing symptoms for months after recovery. We assessed immune activation at 3-12 months post hospital admission in 187 samples from 63 patients with mild, moderate, or severe disease and investigated whether it associates with long COVID. At 3 months, patients with severe disease displayed persistent activation of CD4+ and CD8+ T-cells, based on expression of HLA-DR, CD38, Ki67, and granzyme B, and elevated plasma levels of interleukin-4 (IL-4), IL-7, IL-17, and tumor necrosis factor-alpha (TNF-α) compared to mild and/or moderate patients. Plasma from severe patients at 3 months caused T-cells from healthy donors to upregulate IL-15Rα, suggesting that plasma factors in severe patients may increase T-cell responsiveness to IL-15-driven bystander activation. Patients with severe disease reported a higher number of long COVID symptoms which did not however correlate with cellular immune activation/pro-inflammatory cytokines after adjusting for age, sex, and disease severity. Our data suggests that long COVID and persistent immune activation may correlate independently with severe disease.

Putting the brakes on a cycle: bottom-up effects damp cycle amplitude.

Pest population density oscillations have a profound effect on agroecosystem functioning, particularly when pests cycle with epidemic persistence. Here, we ask whether landscape-level manipulations can be used to restrict the cycle amplitude of the European corn borer moth [Ostrinia nubilalis (Hübner)], an economically important maize pest. We analysed time series from Minnesota (1963-2009) and Wisconsin (1964-2009) to quantify the extent of regime change in the US Corn Belt where rates of transgenic Bt maize adoption varied. The introduction of Bt maize explained cycle damping when the adoption of the crop was high (Minnesota); oscillations were damped but continued to persist when Bt maize was used less intensely (Wisconsin). We conclude that host plant quality is key to understanding both epidemic persistence and the success of intervention strategies. In particular, the dichotomy in maize management between states is thought to limit the spatial autocorrelation of O. nubilalis.

Communicating carabids: Engaging farmers to encourage uptake of integrated pest management.

BACKGROUND: Natural enemy pest control is becoming more desirable as restrictions increase on pesticide use. Carabid beetles are proven agents of natural-enemy pest control (NPC), controlling pests and weeds in crop areas. Agro-ecological measures can be effective for boosting carabid abundance and associated NPC, but the benefits of specific interventions to production are seldom communicated to farmers. We explore pathways to improved NPC by engaging farmers and increasing knowledge about farm management practices (FMPs) beneficial to carabids using engagement materials. We used a questionnaire to measure awareness, beliefs and attitudes to carabids and analysed these within a framework of the Theory of Planned Behaviour (TPB), relative to a control group. RESULTS: We found awareness of carabid predation to be associated with beliefs of pest and weed control efficacy. Within the framework of TPB, we found that current implementation of FMPs was higher if farmers perceived them to be both important for carabids and easy to implement. This was also true for future intention to implement, yet the perceived importance was influenced by engagement materials. Field margins/buffer strips and beetle banks (16% and 13% of responses) were the most favoured by farmers as interventions for carabids. CONCLUSION: The TPB is a valuable tool with which to examine internal elements of farmer behaviour. In this study self-selected participants were influenced by online engagement in a single intervention, proving this approach has the potential to change behaviour. Our results are evidence for the effectiveness of raising awareness of NPC to change attitudes and increase uptake of sustainable practices.

Communication and building social capital in community supported agriculture.

Community supported agriculture (CSA) schemes (programs) provide an alternative means for obtaining produce, through direct purchase from farms. They are also often driven by a vision of transforming the current mainstream food system and seek to build a community of people who support this vision. Social capital refers to the networks and ties between people and groups and the impact of these ties on access to influence, information, opportunity, and ability to organize. Social capital is built by CSAs and helps foster and stabilize the grassroots agricultural innovations that are needed for the development of sustainable food systems. Using the concept of social capital, we studied communication methods of four CSAs in the UK, examining the interactions between CSAs and their members and within each of their membership groups. We carried out in-depth interviews with 49 CSA members to establish what interactions they had with their CSA and with other members, and analyzed our data thematically to identify the characteristics of interactions that were important to participants. We consider how our research may benefit CSA organizations by enabling them to learn what their members want and to learn about the varied ways in which members conceptualize their experiences of community derived from their membership. We found that the various CSA communication strategies, which consist of frequent and varying virtual and face-to-face interactions, are able to promote development of both bridging and bonding social capital. Overall, there is a desire for social connection in CSA memberships. Furthermore, in CSAs where members can interact easily, there is potential for CSA membership to provide members with communication that is important as a source of both knowledge and social connection. CSAs can maximize both social capital and member satisfaction by using a range of communication media and methods to meet their members' circumstances and preferences.

Exploring the effects of land management change on productivity, carbon and nutrient balance: Application of an Ensemble Modelling Approach to the upper River Taw observatory, UK.

Agriculture is challenged to produce healthy food and to contribute to cleaner energy whilst mitigating climate change and protecting ecosystems. To achieve this, policy-driven scenarios need to be evaluated with available data and models to explore trade-offs with robust accounting for the uncertainty in predictions. We developed a novel model ensemble using four complementary state-of-the-art agroecosystems models to explore the impacts of land management change. The ensemble was used to simulate key agricultural and environmental outputs under various scenarios for the upper River Taw observatory, UK. Scenarios assumed (i) reducing livestock production whilst simultaneously increasing the area of arable where it is feasible to cultivate (PG2A), (ii) reducing livestock production whilst simultaneously increasing bioenergy production in areas of the catchment that are amenable to growing bioenergy crops (PG2BE) and (iii) increasing both arable and bioenergy production (PG2A + BE). Our ensemble approach combined model uncertainty using the tower property of expectation and the law of total variance. Results show considerable uncertainty for predicted nutrient losses with different models partitioning the uncertainty into different pathways. Bioenergy crops were predicted to produce greatest yields from Miscanthus in lowland and from SRC-willow (cv. Endurance) in uplands. Each choice of management is associated with trade-offs; e.g. PG2A results in a significant increase of edible calories (6736 Mcal ha-1) but reduced soil C (-4.32 t C ha-1). Model ensembles in the agroecosystem context are difficult to implement due to challenges of model availability and input and output alignment. Despite these challenges, we show that ensemble modelling is a powerful approach for applications such as ours, offering benefits such as capturing structural as well as data uncertainty and allowing greater combinations of variables to be explored. Furthermore, the ensemble provides a robust means for combining uncertainty at different scales and enables us to identify weaknesses in system understanding.