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Vegetation phenology plays a key role in controlling the seasonality of ecosystem processes that modulate carbon, water and energy fluxes between the biosphere and atmosphere. Accurate modelling of vegetation phenology in the interplay of Earth's surface and the atmosphere is thus crucial to understand how the coupled system will respond to and shape climatic changes. Phenology is controlled by meteorological conditions at different timescales: on the one hand, changes in key meteorological variables (temperature, water, radiation) can have immediate effects on the vegetation development; on the other hand, phenological changes can be driven by past environmental conditions, known as memory effects. However, the processes governing meteorological memory effects on phenology are not completely understood, resulting in their limited performance of vegetation phenology represented in land surface models. A deep learning model, specifically a long short-term memory network (LSTM), has the potential to capture and model the meteorological memory effects on vegetation phenology. Here, we apply the LSTM to model the vegetation phenology using meteorological drivers and high-temporal-resolution canopy greenness observations through digital repeat photography by the PhenoCam network. We compare a multiple linear regression model, a no-memory-effect LSTM model and a full-memory-effect LSTM model to predict the whole seasonal greenness trajectory and the corresponding phenological transition dates across 50 sites and 317 site years during 2009–2018, covering deciduous broadleaf forests, evergreen needleleaf forests and grasslands. Results show that the deep learning model outperforms the multiple linear regression model, and the full-memory-effect LSTM model performs better than the no-memory-effect model for all three plant function types (median R2 of 0.878, 0.957 and 0.955 for broadleaf forests, evergreen needleleaf forests and grasslands). We also find that the full-memory-effect LSTM model is capable of predicting the seasonal dynamic variations of canopy greenness and reproducing trends in shifting phenological transition dates. We also performed a sensitivity analysis of the full-memory-effect LSTM model to assess its plausibility, revealing its coherence with established knowledge of vegetation phenology sensitivity to meteorological conditions, particularly changes in temperature. Our study highlights that (1) multi-variate meteorological memory effects play a crucial role in vegetation phenology, and (2) deep learning opens up new avenues for improving the representation of vegetation phenological processes in land surface models via a hybrid modelling approach. [ABSTRACT FROM AUTHOR]

Current strategies to hold surface warming below a certain level, e. g., 1.5 or 2°C, advocate limiting total anthropogenic cumulative carbon emissions to ∼0.9 or ∼1.25 Eg C (1018 grams carbon), respectively. These allowable emission budgets are based on a near-linear relationship between cumulative emissions and warming identified in various modeling efforts. The IPCC assesses this near-linear relationship with high confidence in its Summary for Policymakers (§D1.1 and Figure SPM.10). Here we test this proportionality in specially designed simulations with a latest-generation Earth system model (ESM) that includes an interactive carbon cycle with updated terrestrial ecosystem processes, and a suite of CMIP simulations (ZecMIP, ScenarioMIP). We find that atmospheric CO2 concentrations can differ by ∼100 ppmv and surface warming by ∼0.31°C (0.46°C over land) for the same cumulated emissions (≈1.2 Eg C, approximate carbon budget for 2°C target). CO2 concentration and warming per 1 Eg of emitted carbon (Transient Climate Response to Cumulative Carbon Emissions; TCRE) depend not just on total emissions, but also on the timing of emissions, which heretofore have been mainly overlooked. A decomposition of TCRE reveals that oceanic heat uptake is compensating for some, but not all, of the pathway dependence induced by the carbon cycle response. The time dependency clearly arises due to lagged carbon sequestration processes in the oceans and specifically on land, viz., ecological succession, land-cover, and demographic changes, etc., which are still poorly represented in most ESMs. This implies a temporally evolving state of the carbon system, but one which surprisingly apportions carbon into land and ocean sinks in a manner that is independent of the emission pathway. Therefore, even though TCRE differs for different pathways with the same total emissions, it is roughly constant when related to the state of the carbon system, i. e., the amount of carbon stored in surface sinks. While this study does not fundamentally invalidate the established TCRE concept, it does uncover additional uncertainties tied to the carbon system state. Thus, efforts to better understand this state dependency with observations and refined models are needed to accurately project the impact of future emissions. [ABSTRACT FROM AUTHOR]

The usual homeopathic remedy, "globules," does not contain any pharmacologically active ingredient. However, many patients and practitioners report beneficial effects of homeopathic treatment on various health outcomes. Experimental and clinical research of the last two decades analyzing the underlying mechanisms of the placebo effect could explain this phenomenon, with patients' treatment expectations as the predominant mechanism. Treatment expectations can be optimized through various factors, such as prior information, communication, and treatment context. This narrative review analyses how homeopathy successfully utilizes these factors. Subsequently, it is discussed what evidence-based medicine could learn from homeopathic practice to optimize treatment expectations (e.g., using an empathic, patientcentered communication style, deliberately selecting objects in practice rooms, or using clear treatment rituals and salient contextual stimuli) and thereby treatment effectiveness. Homeopathic remedy does not work beyond the placebo effect but is recommended or prescribed as an active treatment by those who believe in it. Thus, practitioners need to understand the manner in which homeopathy (as an example of inert treatment) works and are advised to reintegrate its underlying effective placebo mechanisms into evidence-based medicine. This promises to increase treatment efficacy, tolerability, satisfaction, and compliance with evidence-based treatments, and addresses the desires patients are trying to satisfy in homeopathy in an ethical, fully informed way that is grounded in evidence-based medicine. [ABSTRACT FROM AUTHOR]

The land sink of anthropogenic carbon emissions, a crucial component of mitigating climate change, is primarily attributed to the CO2 fertilization effect on global gross primary productivity (GPP). However, direct observational evidence of this effect remains scarce, hampered by challenges in disentangling the CO2 fertilization effect from other long‐term confounding drivers, particularly climatic changes. Here, we introduce a novel statistical approach to separate the CO2 fertilization effect on photosynthetic carbon uptake using eddy covariance (EC) records across 38 extratropical forest sites. We find the median stimulation rate of GPP to be 3.2 ± 0.9 gC m−2 yr−1 ppm−1 (or 16.4 ± 4.2% per 100 ppm) under increasing atmospheric CO2 across these sites, respectively. To validate the robustness of our findings, we test our statistical method using factorial simulations of an ensemble of process‐based land surface models. We address additional factors, including nitrogen deposition and land management, that may impact plant productivity, potentially confounding the attribution to the CO2 fertilization effect. Assuming these site‐specific effects offset to some extent across sites as random factors, the estimated median value still reflects the strength of the CO2 fertilization effect. However, disentanglement of these long‐term effects, often inseparable by timescale, requires further causal research. Our study provides direct evidence that the photosynthetic stimulation is maintained under long‐term CO2 fertilization across multiple EC sites. Such observation‐based quantification is key to constraining the long‐standing uncertainties in the land carbon cycle under rising CO2 concentrations. Plain Language Summary: Through photosynthesis, plants convert CO2 and water into sugars and oxygen using solar energy, one of the most important chemical reactions on Earth. Human‐made carbon emissions are increasing atmospheric CO2 levels, impacting global photosynthesis. The additional carbon is believed to have a fertilizing effect on photosynthesis, causing vegetation to absorb a significant portion of the emitted CO2. However, the strength of this CO2 fertilization effect on photosynthesis is uncertain, but is a crucial factor in determining the future trajectory of atmospheric CO2 concentrations. In this study, we introduce a new statistical method to quantify the increase in photosynthetic carbon uptake, stimulated by rising CO2, based on measurements from 38 forest sites. Our results reveal that a 100 ppm increase in CO2 enhances photosynthesis by approximately 16%. Testing the statistical method with artificial model experiments supports the robustness of our findings. Our study improves the understanding of the impacts of human‐made CO2 emissions on the global carbon cycle. Key Points: We present a novel statistical method to disentangle the variability of photosynthetic rates related to climate and non‐climate driversThe analysis from 38 eddy covariance sites reveals a 3.2 ± 0.9 gC m−2 yr−1 increase in plant productivity per ppm rise in CO2Our statistical method is successfully tested against idealized model simulations with and without increasing CO2 [ABSTRACT FROM AUTHOR]

Elasmobranchs are slow‐growing marine predators whose populations have declined due to their susceptibility to overfishing, especially affecting species of large size. Rostroraja alba is a large skate that is currently classified as critically endangered.To address the effects of overfishing and promote the recovery of marine species and habitats, marine protected areas (MPAs) are a commonly implemented management strategy. A critical aspect of their success is using information on species' movement patterns; however, this is frequently not assessed for elasmobranchs.In this study, acoustic telemetry was used to monitor 30 R. alba in the Professor Luís Saldanha Marine Park (Portugal) to assess their long‐term movements in the context of a coastal MPA and to evaluate the contribution of this kind of spatial management to the conservation of this species.Immature individuals were more frequently captured than adults, which postulates this area as a potential nursery. The average residency was moderate and relatively consistent throughout the year, as was area use. No marked size or sex‐based differences in movement were noted. Individuals were more active and occurred in shallower waters at night and twilight, and occasional quick vertical movements into the water column, isolated or in sequence, were also noted.In general, the LSMP seems to offer stable protection to immature R. alba, with no strong seasonal variation. These results provide relevant input for adaptive management measures of this and other similar MPAs and highlight the contribution of these efforts to the protection of nursery areas. [ABSTRACT FROM AUTHOR]

Background: The meagre, Argyrosomus regius, is a large coastal predatory fish inhabiting waters from the north-eastern Atlantic and Mediterranean Sea, where it is targeted by commercial and recreational fisheries. Previous genetic studies have found an unexpectedly high population differentiation not only between the Atlantic and the Mediterranean, but also along the Atlantic coast. However, the reasons underpinning this genetic barrier remained unclear. Likewise, even though the species is amongst the world's largest marine teleosts, knowledge about its movement ecology and migratory behaviour remains notably scarce, and primarily reliant on fisheries-dependent data. Methods: In this study, we used a combination of acoustic telemetry and pop-up satellite archival tags to investigate the movements of 22 adult meagre (70–143 cm total length) along the Southwestern coast of the Iberian Peninsula. Results: Our results strongly suggest that the previously reported genetic differentiation is not maintained by limited adult dispersal/movement, as hypothesized. On the contrary, we documented some of the longest individual annual migrations ever recorded for a coastal teleost, up to > 2000 km, with frequent back-and-forth movements between the West and Southern Iberian coasts. Moreover, their detected regional movement patterns support the existence of a marked seasonal behavioural shift, with individuals being less active and moving to deeper waters during winter, and are consistent with spawning philopatry associated to their summer reproductive movements. Finally, we identified putative aggregation areas that may harbour important feeding/overwintering grounds. Conclusions: These findings shed new light on the movement and behaviour patterns of meagre that may be of particular importance for the conservation and spatial management of this species throughout its range, and open the door to further research on functional connectivity. [ABSTRACT FROM AUTHOR]

Vegetation phenology plays a key role in controlling the seasonality of ecosystem processes that modulate carbon, water and energy fluxes between biosphere and atmosphere. Accurate modelling of vegetation phenology in the interplay of Earth's surface and the atmosphere is thus crucial to understand how the coupled system will respond to and shape climatic changes. Phenology is controlled by meteorological conditions at different time scales: on the one hand, changes in key meteorological variables (temperature, water, radiation) can have immediate effects on the vegetation development; on the other hand, phenological changes can be driven by past environmental conditions, known as memory effects. However, the processes governing meteorological memory effects on phenology are not completely understood, resulting in their limited performance of phenology simulated by land surface models. A deep learning model, specifically a long short-term memory network (LSTM), has the potential to capture and model the meteorological memory effects on vegetation phenology. Here, we apply the LSTM to model the vegetation phenology using meteorological drivers and canopy greenness at high temporal resolution collected taking advantage of digital repeat photography by the PhenoCam network. We compare a simple multiple linear regression model, a no-memory-effect, and a full-memory-effect LSTM model to predict the whole seasonal greenness trajectory and the corresponding phenological transition dates of 50 sites and 317 site-year during 2009–2018, across deciduous broadleaf forests, evergreen needleleaf forests and grasslands. The deep learning model outperforms the multiple linear regression model, and the full-memory-effect LSTM model performs better than no-memory-effect model for all three plant function types (median R2 of 0.878, 0.957, and 0.955 for broadleaf forests, evergreen needleleaf forests and grasslands) corroborating the benefits of deep learning approach and the importance of multi-variate meteorological memory effects in phenology modelling. We also find that the LSTM model is capable of predicting the seasonal dynamic variations of canopy greenness and reproducing trends in shifting phenological transition dates. We also performed a sensitivity analysis of the LSTM model to assess its plausibility, revealing its coherence with established knowledge of vegetation phenology sensitivity to meteorological conditions, particularly changes in temperature. Our study highlights that 1) multi-variate meteorological memory effects play a crucial role in vegetation phenology, and 2) deep learning opens up new avenues for improving the representation of vegetation phenological processes in land surface models via a hybrid modelling approach. [ABSTRACT FROM AUTHOR]

Fire is a crucial factor in terrestrial ecosystems playing a role in disturbance for vegetation dynamics. Process‐based fire models quantify fire disturbance effects in stand‐alone dynamic global vegetation models (DGVMs) and their advances have incorporated both descriptions of natural processes and anthropogenic drivers. Nevertheless, these models show limited skill in modeling fire events at the global scale, due to stochastic characteristics of fire occurrence and behavior as well as the limits in empirical parameterizations in process‐based models. As an alternative, machine learning has shown the capability of providing robust diagnostics of fire regimes. Here, we develop a deep‐learning‐based fire model (DL‐fire) to estimate daily burnt area fraction at the global scale and couple it within JSBACH4, the land surface model used in the ICON‐ESM. The stand‐alone DL‐fire model forced with meteorological, terrestrial and socio‐economic variables is able to simulate global total burnt area, showing 0.8 of monthly correlation (rm) with GFED4 during the evaluation period (2011–2015). The performance remains similar with the hybrid modeling approach JSB4‐DL‐fire (rm = 0.79) outperforming the currently used uncalibrated standard fire model in JSBACH4 (rm = −0.07). We further quantify the importance of each predictor by applying layer‐wise relevance propagation (LRP). Overall, land properties, such as fuel amount and water content in soil layers, stand out as the major factors determining burnt fraction in DL‐fire, paralleled by meteorological conditions over tropical and high latitude regions. Our study demonstrates the potential of hybrid modeling in advancing fire prediction in ESMs by integrating deep learning approaches in physics‐based dynamical models. Plain Language Summary: We develop a fire‐vegetation model based on a hybrid approach integrating artificial intelligence (AI) techniques into physics‐based models. Given the weather conditions, vegetation states, and human factors, our model estimates daily burned area fraction. The spatiotemporal variations in burned area are closely reproduced, especially over fire‐prone regions, such as Africa, South America, and Australia. Our model is able to represent regional variations in the drivers of fire occurrence, showing different importance of input predictors for different regions. This approach shows the possibilities of using deep learning (DL) models to provide in‐depth fire predictions in Earth system models. Key Points: Deep neural networks (DNN) can accurately predict global burnt area fraction on a daily scaleIntegration of the DNN into a physics‐based land model significantly improves the estimation of biomass burnt damage in vegetation dynamicsThe DNN accounts for regional fire variations by assigning varying degrees of importance to each predictor [ABSTRACT FROM AUTHOR]

Argyrosomus regius (commonly referred to as meagre), is one of Europe's largest coastal bony fish species and supports important recreational and commercial fisheries in the Atlantic and Mediterranean coasts. Demand for this species, and more recently for their swim bladders, has led to regional population declines and growing importance as an aquaculture species. Despite intense research in captivity, little is known about the spatial ecology of A. regius's wild population, including basic information such as vertical migrations and depth/temperature preferences. Previous research based on indirect data suggests a seasonal habitat shift from shallow to deeper waters, but this has never been validated through direct high-resolution movement data. In this study, we tagged 13 adult A. regius with pop-up satellite archival tags in the South of Portugal, which successfully returned data from 11 individuals including high-resolution data from six recovered tags (mean, range: 167 days, 28–301 days). We found that adults of this population spend 95.2% of their time between 5 and 75 m depth (mean ± SD, 30.9m ± 18.3m) and do not venture beyond 125 m. Across seasons, A. regius move across water temperatures between 13.3 and 24.8°C with a preferred thermal range between 14 and 18°C where they spent 75.4% of their time. The inferential modelling using this electronic data validated previous hypotheses by showing significant differences between a shallower and warmer summer habitat vs. a deeper and cooler winter habitat. Visual investigation of the diel effects on depth preferences suggests subtle changes in depth use between day and night during the warmer months of the year. We speculate that these patterns are in response to the species' behavioural ecology and physiology, reflecting the seasonal changes in water stratification and presence of prey, as well as on the species reproduction, which results in summer spawning aggregations in shallower areas. [ABSTRACT FROM AUTHOR]

While numerous studies report shifts in vegetation phenology, in this regard eddy covariance (EC) data, despite its continuous high-frequency observations, still requires further exploration. Furthermore, there is no general consensus on optimal methodologies for data smoothing and extracting phenological transition dates (PTDs). Here, we revisit existing methodologies and present new prospects to investigate phenological changes in gross primary productivity (GPP) from EC measurements. First, we present a smoothing technique of GPP time series through the derivative of its smoothed annual cumulative sum. Second, we calculate PTDs and their trends from a commonly used threshold method that identifies days with a fixed percentage of the annual maximum GPP. A systematic analysis is performed for various thresholds ranging from 0.1 to 0.7. Lastly, we examine the relation of PTDs trends to trends in GPP across the years on a weekly basis. Results from 47 EC sites with long time series (> 10 years) show that advancing trends in start of season (SOS) are strongest at lower thresholds but for the end of season (EOS) at higher thresholds. Moreover, the trends are variable at different thresholds for individual vegetation types and individual sites, outlining reasonable concerns on using a single threshold value. Relationship of trends in PTDs and weekly GPP reveal association of advanced SOS and delayed EOS to increase in immediate primary productivity, but not to the trends in overall seasonal productivity. Drawing on these analyses, we emphasise on abstaining from subjective choices and investigating relationship of PTDs trend to finer temporal trends of GPP. Our study examines existing methodological challenges and presents approaches that optimize the use of EC data in identifying vegetation phenological changes and their relation to carbon uptake. [ABSTRACT FROM AUTHOR]

In this paper, an extension of an algorithm for the implicit discretization of a super‐twisting sliding mode observer is presented. Implicit and explicit discretization algorithms for homogeneous differentiators, where no physical model information is considered, are investigated in literature. This article studies the behavior when considering models of a rather general class of nonlinear systems. The discrete equations of the super‐twisting observer are reformulated as generalized equation and an algorithm for the step‐by‐step solution is given. The uniqueness of the derived algorithm is investigated with an equivalent variational inequality formulation which is derived for a class of nonlinear systems. Furthermore, a semi‐implicit predictor‐corrector discretization is presented which is an approximation method for the presented algorithms and allows an explicit implementation in practical applications. Accuracy properties under noise and sampling are given. The algorithm is applied on two mechanical example systems taken from practice. [ABSTRACT FROM AUTHOR]

While numerous studies report shifts in vegetation phenology, in this regard eddy covariance (EC) data, despite its continuous high-frequency observations, still requires further exploration. Furthermore, there is no general consensus on optimal methodologies for data smoothing and extracting phenological transition dates (PTDs). Here, we revisit existing methodologies and present new prospects to investigate phenological changes in gross primary productivity (GPP) from EC measurements. First, we present a smoothing technique of GPP time series through the derivative of its smoothed annual cumulative sum. Second, we calculate PTDs and their trends from a commonly used threshold method that identifies days with a fixed percentage of the annual maximum GPP. A systematic analysis is performed for various thresholds ranging from 0.1 to 0.7. Lastly, we examine the relation of PTDs trends to trends in GPP across the years on a weekly basis. Results from 47 EC sites with long time series (> 10 years) show that advancing trends in start of season (SOS) are strongest at lower thresholds but for the end of season (EOS) at higher thresholds. Moreover, the trends are variable at different thresholds for individual vegetation types and individual sites, outlining reasonable concerns on using a single threshold value. Relationship of trends in PTDs and weekly GPP reveal association of advanced SOS and delayed EOS to increase in immediate primary productivity, but not to the trends in overall seasonal productivity. Drawing on these analyses, we emphasise on abstaining from subjective choices and investigating relationship of PTDs trend to finer temporal trends of GPP. Our study examines existing methodological challenges and presents approaches that optimize the use of EC data in identifying vegetation phenological changes and their relation to carbon uptake. [ABSTRACT FROM AUTHOR]

Purpose: Only a few studies have evaluated Augmented Reality (AR) in in vivo simulations compared to traditional laparoscopy; further research is especially needed regarding the most effective AR visualization technique. This pilot study aims to determine, under controlled conditions on a 3D-printed phantom, whether an AR laparoscope improves surgical outcomes over conventional laparoscopy without augmentation. Methods: We selected six surgical residents at a similar level of training and had them perform a laparoscopic task. The participants repeated the experiment three times, using different 3D phantoms and visualizations: Floating AR, Occlusion AR, and without any AR visualization (Control). Surgical performance was determined using objective measurements. Subjective measures, such as task load and potential application areas, were collected with questionnaires. Results: Differences in operative time, total touching time, and SurgTLX scores showed no statistical significance ( p > 0.05 ). However, when assessing the invasiveness of the simulated intervention, the comparison revealed a statistically significant difference ( p = 0.009 ). Participants felt AR could be useful for various surgeries, especially for liver, sigmoid, and pancreatic resections (100%). Almost all participants agreed that AR could potentially lead to improved surgical parameters, such as operative time (83%), complication rate (83%), and identifying risk structures (83%). Conclusion: According to our results, AR may have great potential in visceral surgery and based on the objective measures of the study, may improve surgeons' performance in terms of an atraumatic approach. In this pilot study, participants consistently took more time to complete the task, had more contact with the vascular tree, were significantly more invasive, and scored higher on the SurgTLX survey than with AR. [ABSTRACT FROM AUTHOR]

Introduction: This study compares five augmented reality (AR) vasculature visualization techniques in a mixed-reality laparoscopy simulator with 50 medical professionals and analyzes their impact on the surgeon. Material and methods: ​​The different visualization techniques' abilities to convey depth were measured using the participant's accuracy in an objective depth sorting task. Demographic data and subjective measures, such as the preference of each AR visualization technique and potential application areas, were collected with questionnaires. Results: Despite measuring differences in objective measurements across the visualization techniques, they were not statistically significant. In the subjective measures, however, 55% of the participants rated visualization technique II, 'Opaque with single-color Fresnel highlights', as their favorite. Participants felt that AR could be useful for various surgeries, especially complex surgeries (100%). Almost all participants agreed that AR could potentially improve surgical parameters, such as patient safety (88%), complication rate (84%), and identifying risk structures (96%). Conclusions: More studies are needed on the effect of different visualizations on task performance, as well as more sophisticated and effective visualization techniques for the operating room. With the findings of this study, we encourage the development of new study setups to advance surgical AR. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the clinical outcome of root canal treatment by obturation technique, root canal filling quality, and tooth- and patient-related variables. Methods: This clinical study investigated the endodontic outcome of 114 teeth treated between the years 2009 and 2012. Three different obturation methods were used: (1) adhesive obturation using the continuous wave of condensation technique with Resilon® (CWR), (2) matching-taper single-cone technique with gutta-percha and AH Plus® (SCGP), and (3) matching-taper single-cone technique with gutta-percha and GuttaFlow® (SCGF). Pre- and postoperative periapical radiographs were performed to detect the presence of endodontic lesions (PAI classification) and to assess the quality of both the obturation and the restoration. Tooth- and patient-related data were collected. Results: The overall endodontic success rate was 75.4% after a mean observation period of 6.3 years. There were no significant correlations between the type or overall quality of obturation and the treatment outcome. Teeth with preoperative lesions had the highest odds ratio (factor of 4.98) for endodontic failure. Tooth- and patient-related variables had no significant effect on endodontic outcome. Conclusions: The preoperative periapical status of teeth requiring endodontic treatment was a substantial prognostic factor for endodontic outcome, whereas the type of obturation material or technique did not affect it. [ABSTRACT FROM AUTHOR]

Background: Analysis of surgical instrument motion is applicable in surgical skill assessment and monitoring of the learning progress in laparoscopy. Current commercial instrument tracking technology (optical or electromagnetic) has specific limitations and is expensive. Therefore, in this study, we apply inexpensive, off-the-shelf inertial sensors to track laparoscopic instruments in a training scenario. Methods: We calibrated two laparoscopic instruments to the inertial sensor and investigated its accuracy on a 3D-printed phantom. In a user study during a one-week laparoscopy training course with medical students and physicians, we then documented and compared the training effect in laparoscopic tasks on a commercially available laparoscopy trainer (Laparo Analytic, Laparo Medical Simulators, Wilcza, Poland) and the newly developed tracking setup. Results: Eighteen participants (twelve medical students and six physicians) participated in the study. The student subgroup showed significantly poorer results for the count of swings (CS) and count of rotations (CR) at the beginning of the training compared to the physician subgroup (p = 0.012 and p = 0.042). After training, the student subgroup showed significant improvements in the rotatory angle sum, CS, and CR (p = 0.025, p = 0.004 and p = 0.024). After training, there were no significant differences between medical students and physicians. There was a strong correlation between the measured learning success (LS) from the data of our inertial measurement unit system (LSIMU) and the Laparo Analytic (LSLap) (Pearson's r = 0.79). Conclusion: In the current study, we observed a good and valid performance of inertial measurement units as a possible tool for instrument tracking and surgical skill assessment. Moreover, we conclude that the sensor can meaningfully examine the learning progress of medical students in an ex-vivo setting. [ABSTRACT FROM AUTHOR]

Open-label placebo (OLP) may be utilized to reduce psychological distress. Yet, potential contextual effects have not been explored. We investigated the impact of pharmaceutical form and the simulation of side effects in a parallel group RCT (DRKS00030987). A sample of 177 highly stressed university students at risk of depression were randomly assigned by computer generated tables to a 1-week intervention with active or passive OLP nasal spray or passive OLP capsule or a no-treatment control group. After the intervention, groups differed significantly in depressive symptoms but not regarding other outcomes of psychological distress (stress, anxiety, sleep quality, somatization), well-being or treatment expectation. OLP groups benefitted significantly more compared to the no-treatment control group (d =.40), OLP nasal spray groups significantly more than the OLP capsule group (d =.40) and the active OLP group significantly more than the passive OLP groups (d =.42). Interestingly, before intervention, most participants, regardless of group assignment, believed that the OLP capsule would be most beneficial. The effectiveness of OLP treatments seems to be highly influenced by the symptom focus conveyed by the OLP rationale. Moreover, pharmaceutical form and simulation of side effects may modulate efficacy, while explicit treatment expectation seems to play a minor role. [ABSTRACT FROM AUTHOR]

Background: The clinical management of teeth with complex dens invaginatus (DI) malformations and apical periodontitis may be challenging due to the lack of routine. The aim of this case report is to describe the endodontic treatment of an immature tooth with DI and to discuss strategies for preclinical training for teeth with such malformations. Case report: A 9-year-old male presented with an immature maxillary incisor with DI (Oehlers Type II) and apical periodontitis which was diagnosed by cone beam computed tomography (CBCT). Revitalization was initially attempted but then abandoned after failure to generate a stable blood clot. Nevertheless, considerable increase in both root length and thickness could be detected after medication with calcium hydroxide followed by root canal filling with MTA as an apical plug. Conclusions: The endodontic management of teeth with DI requires thorough treatment planning. In immature teeth, under certain conditions, root maturation may occur even with conventional apexification procedures. From an educational perspective, different strategies including CBCT and 3D-printed transparent tooth models for visualization of the complex internal morphology and redesigned 3D-printed replica with various degrees of difficulty for endodontic training, can be used to overcome the challenges associated with endodontic treatment of such teeth. [ABSTRACT FROM AUTHOR]

Three decades after the first set of work on Medical Augmented Reality (MAR) was presented to the international community, and ten years after the deployment of the first MAR solutions into operating rooms, its exact definition, basic components, systematic design, and validation still lack a detailed discussion. This paper defines the basic components of any Augmented Reality (AR) solution and extends them to exemplary Medical Augmented Reality Systems (MARS). We use some of the original MARS applications developed at the Chair for Computer Aided Medical Procedures and deployed into medical schools for teaching anatomy and into operating rooms for telemedicine and surgical guidance throughout the last decades to identify the corresponding basic components. In this regard, the paper is not discussing all past or existing solutions but only aims at defining the principle components and discussing the particular domain modeling for MAR and its design-development-validation process, and providing exemplary cases through the past in-house developments of such solutions. [ABSTRACT FROM AUTHOR]

Anthropogenic-induced climate change is having profound impacts on aquatic ecosystems, and the resilience of fish populations will be determined by their response to these impacts. The northern Namibian coast is an ocean warming hotspot, with temperatures rising faster than the global average. The rapid warming in Namibia has had considerable impacts on marine fauna, such as the southern extension of the distribution of Argyrosomus coronus from southern Angola into northern Namibian waters, where it now overlaps and hybridizes with the closely related Namibian species, A. inodorus. Understanding how these species (and their hybrids) perform at current and future temperatures is vital to optimize adaptive management for Argyrosomus species. Intermittent flow-through respirometry was used to quantify standard and maximum metabolic rates for Argyrosomus individuals across a range of temperatures. The modelled aerobic scope (AS) of A. inodorus was notably higher at cooler temperatures (12, 15, 18 and 21°C) compared with that of A. coronus , whereas the AS was similar at 24°C. Although only five hybrids were detected and three modelled, their AS was in the upper bounds of the models at 15, 18 and 24°C. These findings suggest that the warming conditions in northern Namibia may increasingly favour A. coronus and promote the poleward movement of the leading edge of their southern distribution. In contrast, the poor aerobic performance of both species at cold temperatures (12°C) suggests that the cold water associated with the permanent Lüderitz Upwelling Cell in the south may constrain both species to central Namibia. This is most concerning for A. inodorus because it may be subjected to a considerable coastal squeeze. [ABSTRACT FROM AUTHOR]

Elevated atmospheric CO2 (eCO2) influences the carbon assimilation rate and stomatal conductance of plants, thereby affecting the global cycles of carbon and water. Yet, the detection of these physiological effects of eCO2 in observational data remains challenging, because natural variations and confounding factors (e.g., warming) can overshadow the eCO2 effects in observational data of real‐world ecosystems. In this study, we aim at developing a method to detect the emergence of the physiological CO2 effects on various variables related to carbon and water fluxes. We mimic the observational setting in ecosystems using a comprehensive process‐based land surface model QUINCY to simulate the leaf‐level effects of increasing atmospheric CO2 concentrations and their century‐long propagation through the terrestrial carbon and water cycles across different climate regimes and biomes. We then develop a statistical method based on the signal‐to‐noise ratio to detect the emergence of the eCO2 effects. The eCO2 effect on gross primary productivity (GPP) emerges at relatively low CO2 increase (∆[CO2] ~ 20 ppm) where the leaf area index is relatively high. Compared to GPP, the eCO2 effect causing reduced transpiration water flux (normalized to leaf area) emerges only at relatively high CO2 increase (∆[CO2] >> 40 ppm), due to the high sensitivity to climate variability and thus lower signal‐to‐noise ratio. In general, the response to eCO2 is detectable earlier for variables related to the carbon cycle than the water cycle, when plant productivity is not limited by climatic constraints, and stronger in forest‐dominated rather than in grass‐dominated ecosystems. Our results provide a step toward when and where we expect to detect physiological CO2 effects in in‐situ flux measurements, how to detect them and encourage future efforts to improve the understanding and quantification of these effects in observations of terrestrial carbon and water dynamics. [ABSTRACT FROM AUTHOR]

In this paper, a deep neural network (DNN)-based nonlinear model predictive controller (NMPC) is demonstrated using real-time experimental implementation. First, the emissions and performance of a 4.5-liter 4-cylinder Cummins diesel engine are modeled using a DNN model with seven hidden layers and 24,148 learnable parameters created by stacking six Fully Connected layers with one long-short term memory (LSTM) layer. This model is then implemented as the plant model in an NMPC. For real-time implementation of the LSTM-NMPC, an open-source package acados with the quadratic programming solver HPIPM (High-Performance Interior-Point Method) is employed. This helps LSTM-NMPC run in real time with an average turnaround time of 62.3 milliseconds. For real-time controller prototyping, a dSPACE MicroAutoBox II rapid prototyping system is used. A Field-Programmable Gate Array is employed to calculate the in-cylinder pressure-based combustion metrics online in real time. The developed controller was tested for both step and smooth load reference changes, which showed accurate tracking performance while enforcing all input and output constraints. To assess the robustness of the controller to data outside the training region, the engine speed is varied from 1200 rpm to 1800 rpm. The experimental results illustrate accurate tracking and disturbance rejection for the out-of-training data region. At 5 bar indicated mean effective pressure and a speed of 1200 rpm, the comparison between the Cummins production controller and the proposed LSTM-NMPC showed a 7.9% fuel consumption reduction, while also decreasing both nitrogen oxides ( NO x ) and Particle Matter (PM) by up to 18.9% and 40.8%. [ABSTRACT FROM AUTHOR]

This paper interrogates the concept of transdisciplinarity, both theoretically and practically, from a perspective of early career researchers (ECRs) in transformative ocean governance research. Aiming to advance research methodologies for future complex sustainability challenges, the paper seeks to illuminate some common uncertainties and challenges surrounding transdisciplinarity from a marine science perspective. Following a literature review on transdisciplinary research, workshops, and a series of surveys, we determine that transdisciplinarity appears to be a concept in search of definition, and that there is a need to explore transdisciplinarity specifically from an ocean research perspective. The paper discusses a number of challenges experienced by ECRs in conducting transdisciplinary research and provides recommendations for both ECRs wishing to undertake more equitable transdisciplinary research and for the UN Decade for Ocean Science to support ECRs in this endeavour (Figure 1). Based on our findings, we interrogate the role of non-academic collaborators in transdisciplinary research and argue that future transdisciplinarity will need to address power imbalances in existing research methods to achieve knowledge co -production, as opposed to knowledge integration. [ABSTRACT FROM AUTHOR]

One of the least understood temporal scales of global carbon cycle (C-cycle) dynamics is its interannual variability (IAV). This variability is mainly driven by variations in the local climatic drivers of terrestrial ecosystem activity, which in turn are controlled by large-scale modes of atmospheric variability. Here, we quantify the fraction of global C-cycle IAV that is explained by large-scale atmospheric circulation variability, which is quantified by spatiotemporal sea level pressure (SLP) fields. C-cycle variability is diagnosed from the global detrended atmospheric CO2 growth rate and the land CO2 sink from 16 dynamic global vegetation models and two atmospheric inversions in the Global Carbon Budget 2018. We use a regularized linear regression model, which represents a statistical learning technique apt to deal with the large number of atmospheric circulation predictors (p≥800 , each representing one pixel-based time series of SLP anomalies) in a relatively short observed record (n<60 years). We show that boreal winter and spring SLP anomalies allow predicting IAV in the atmospheric CO2 growth rate and the global land sink, with Pearson correlations between reference and predicted values between 0.70 and 0.84 for boreal winter SLP anomalies. This is comparable to or higher than that of a similar model using 15 traditional teleconnection indices as predictors. The spatial patterns of regression coefficients of the model based on SLP fields show a predominant role of the tropical Pacific and over Southeast Asia extending to Australia, corresponding to the regions associated with the El Niño–Southern Oscillation variability. We also identify another important region in the western Pacific, roughly corresponding to the West Pacific pattern. We further evaluate the influence of the time series length on the predictability of IAV and find that reliable estimates of global C-cycle IAV can be obtained from records of 30–54 years. For shorter time series (n<30 years), however, our results show that conclusions about CO2 IAV patterns and drivers need to be evaluated with caution. Overall, our study illustrates a new data-driven and flexible approach to model the relationship between large-scale atmospheric circulation variations and C-cycle variability at global and regional scales, complementing the traditional use of teleconnection indices. [ABSTRACT FROM AUTHOR]

Changes in behaviour and physiology are the primary responses of fishes to anthropogenic impacts such as climate change and over-fishing. Behavioural changes (such as a shift in distribution or changes in phenology) can ensure that a species remains in an environment suited for its optimal physiological performance. However, if fishes are unable to shift their distribution, they are reliant on physiological acclimatization (either by broadening their metabolic curves to tolerate a range of stressors, or by shifting their metabolic curves to maximize their performance at extreme stressors). However, since there are links between fish physiology and behaviour, changes to either of these trait groups may have reciprocal interactions. This paper reviews the current knowledge of the links between the behaviour and aerobic metabolic physiology of fishes, discusses these in the context of exploitation and climate change and makes recommendations for future research needs. The review revealed that our understanding of the links between fish behaviour and metabolic physiology is rudimentary. However, both are hypothesized to be linked to stress responses along the hypothalamic pituitary axis. The link between metabolic physiological capacity and behaviour is particularly important as both determine the response of an individual to a changing climate and are under selection by fisheries. While, it appears that all types of capture fisheries are likely to reduce the adaptive potential of fished populations to climate stressors, angling, which is primarily associated with recreational fishing, may induce the separation of natural populations by removing individuals with bold behavioural traits and potentially the physiological traits required to facilitate behavioural change. Future research should focus on assessing how the links between metabolic physiological capacity and behaviour influence catchability, the response to climate change drivers and post-release recovery. The plasticity of phenotypic traits should be examined under a range of stressors of differing intensity, in several species and life history stages. Future studies should also assess plasticity (fission or fusion) in the phenotypic structuring of social hierarchy and how this influences habitat selection. Ultimately, to fully understand how physiology is influenced by the selective processes driven by fisheries, long-term monitoring of the physiological and behavioural structure of fished populations, their fitness and catch rates are required. This will provide information that can be used by managers to retain behavioural and physiological trait diversity, which will be necessary to improve the resilience of fished populations to the impacts of climate change and safeguard the provision of resources for future generations. [ABSTRACT FROM AUTHOR]

One of the least understood temporal-scales of global carbon cycle (C-cycle) dynamics is its inter-annual variability (IAV). This variability is mainly driven by variations in the local climatic drivers of terrestrial ecosystem activity, which in turn are controlled by large-scale modes of atmospheric variability. Here, we quantify the fraction of C-cycle IAV that is explained by large-scale atmospheric circulation variability, which is quantified by spatio-temporal sea level pressure (SLP) fields. CO2 variability is diagnosed from the detrended atmospheric CO2 growth rate and the land CO2 sink from different datasets in the global carbon budget. We use a regularized linear regression model, a statistical learning technique, apt to deal with the large number of atmospheric circulation predictors (p = 800, each representing one pixel-based time-series of SLP anomalies) in a relatively short observed record (n < 60 years). We show that boreal winter and spring SLP anomalies allow predicting IAV in atmospheric CO2 growth rate and of the global land sink, with Pearson correlations between reference and predicted values as high as 0.70-0.84 with boreal winter SLP anomalies. This is comparable or higher than that of a similar model using 15 traditional teleconnection indices as predictors. The coefficient patterns of the model based on SLP fields show a predominant role of the tropical Pacific and over Southeast Asia extending to Australia, corresponding to the regions associated with the El Niño/Southern Oscillation variability. We also identify one region in the western Pacific, roughly corresponding to the West Pacific pattern. We further evaluate the influence of the time-series length on the predictability of IAV and find that reliable estimates of C-cycle IAV can be obtained from records of -30-60 years. For shorter time-series (n < 30 years), however, conclusions about CO2 IAV patterns and drivers need to be evaluated with caution. Overall, our study illustrates a new data-driven and flexible approach to model the relationship between large-scale atmospheric circulation variations and C-cycle variability at global and regional scales, complementing the traditional use of teleconnection indices. [ABSTRACT FROM AUTHOR]

One of the least understood temporal–scales of global carbon cycle (C–cycle) dynamics is its inter–annual variability (IAV). This variability is mainly driven by variations in the local climatic drivers of terrestrial ecosystem activity, which in turn are controlled by large–scale modes of atmospheric variability. Here, we quantify the fraction of C–cycle IAV that is explained by large–scale atmospheric circulation variability, which is quantified by spatio–temporal sea level pressure (SLP) fields. CO2 variability is diagnosed from the detrended atmospheric CO2 growth rate and the land CO2 sink from different datasets in the global carbon budget. We use a regularized linear regression model, a statistical learning technique, apt to deal with the large number of atmospheric circulation predictors (p ≥ 800, each representing one pixel–based time–series of SLP anomalies) in a relatively short observed record (n < 60 years). We show that boreal winter and spring SLP anomalies allow predicting IAV in atmospheric CO2 growth rate and of the global land sink, with Pearson correlations between reference and predicted values as high as 0.70–0.84 with boreal winter SLP anomalies. This is comparable or higher than that of a similar model using 15 traditional teleconnection indices as predictors. The coefficient patterns of the model based on SLP fields show a predominant role of the tropical Pacific and over Southeast Asia extending to Australia, corresponding to the regions associated with the El Niño/Southern Oscillation variability. We also identify one region in the western Pacific, roughly corresponding to the West Pacific pattern. We further evaluate the influence of the time–series length on the predictability of IAV and find that reliable estimates of C–cycle IAV can be obtained from records of ~30–60 years. For shorter time–series (n < 30 years), however, conclusions about CO2 IAV patterns and drivers need to be evaluated with caution. Overall, our study illustrates a new data–driven and flexible approach to model the relationship between large–scale atmospheric circulation variations and C–cycle variability at global and regional scales, complementing the traditional use of teleconnection indices. [ABSTRACT FROM AUTHOR]

Online evidence suggests that there has been an increase in interest of using unmanned aerial vehicles or drones during land-based marine recreational fishing. In the absence of reliable monitoring programs, this study used unconventional publicly available online monitoring methodologies to estimate the growing interest, global extent, catch composition and governance of this practice. Results indicated a 357% spike in interest during 2016 primarily in New Zealand, South Africa and Australia. From an ecological perspective, many species targeted by drone fishers are vulnerable to overexploitation, while released fishes may experience heightened stress and mortality. From a social perspective, the ethics of drone fishing are being increasingly questioned by many recreational anglers and we forecast the potential for increased conflict with other beach users. In terms of governance, no resource use legislation specifically directed at recreational drone fishing was found. These findings suggest that drone fishing warrants prioritised research and management consideration. [ABSTRACT FROM AUTHOR]

Satellite data reveal widespread changes in Earth's vegetation cover. Regions intensively attended to by humans are mostly greening due to land management. Natural vegetation, on the other hand, is exhibiting patterns of both greening and browning in all continents. Factors linked to anthropogenic carbon emissions, such as CO2 fertilization, climate change, and consequent disturbances such as fires and droughts, are hypothesized to be key drivers of changes in natural vegetation. A rigorous regional attribution at the biome level that can be scaled to a global picture of what is behind the observed changes is currently lacking. Here we analyze different datasets of decades-long satellite observations of global leaf area index (LAI, 1981–2017) as well as other proxies for vegetation changes and identify several clusters of significant long-term changes. Using process-based model simulations (Earth system and land surface models), we disentangle the effects of anthropogenic carbon emissions on LAI in a probabilistic setting applying causal counterfactual theory. The analysis prominently indicates the effects of climate change on many biomes – warming in northern ecosystems (greening) and rainfall anomalies in tropical biomes (browning). The probabilistic attribution method clearly identifies the CO2 fertilization effect as the dominant driver in only two biomes, the temperate forests and cool grasslands, challenging the view of a dominant global-scale effect. Altogether, our analysis reveals a slowing down of greening and strengthening of browning trends, particularly in the last 2 decades. Most models substantially underestimate the emerging vegetation browning, especially in the tropical rainforests. Leaf area loss in these productive ecosystems could be an early indicator of a slowdown in the terrestrial carbon sink. Models need to account for this effect to realize plausible climate projections of the 21st century. [ABSTRACT FROM AUTHOR]

Background: Anti-glomerular basement membrane disease (GBM) disease is a rare autoimmune disease causing rapidly progressive glomerulonephritis and pulmonary haemorrhage. Recently, an association between COVID-19 and anti-glomerular basement membrane (anti-GBM) disease has been proposed. We report on a patient with recurrence of anti-GBM disease after SARS-CoV-2 infection.Case Presentation: The 31-year-old woman had a past medical history of anti-GBM disease, first diagnosed 11 years ago, and a first relapse 5 years ago. She was admitted with severe dyspnoea, haemoptysis, pulmonary infiltrates and acute on chronic kidney injury. A SARS-CoV-2 PCR was positive with a high cycle threshold. Anti-GBM autoantibodies were undetectable. A kidney biopsy revealed necrotising crescentic glomerulonephritis with linear deposits of IgG, IgM and C3 along the glomerular basement membrane, confirming a recurrence of anti-GBM disease. She was treated with steroids, plasma exchange and two doses of rituximab. Pulmonary disease resolved, but the patient remained dialysis-dependent. We propose that pulmonary involvement of COVID-19 caused exposure of alveolar basement membranes leading to the production of high avidity autoantibodies by long-lived plasma cells, resulting in severe pulmonary renal syndrome.Conclusion: Our case supports the assumption of a possible association between COVID-19 and anti-GBM disease. [ABSTRACT FROM AUTHOR]

Satellite data reveal widespread changes of Earth's vegetation cover. Regions intensively attended to by humans are mostly greening due to land management. Natural vegetation, on the other hand, is exhibiting patterns of both greening and browning in all continents. Factors linked to anthropogenic carbon emissions, such as CO2 fertilization, climate change and consequent disturbances, such as fires and droughts, are hypothesized to be key drivers of changes in natural vegetation. A rigorous regional attribution at biome-level that can be scaled into a global picture of what is behind the observed changes is currently lacking. Here we analyze different datasets of decades-long satellite observations of global leaf area index (LAI, 1981-2017) as well as other proxies of vegetation changes, and identify several clusters of significant long-term changes. Using process-based model simulations (Earth system and land surface models), we disentangle the effects of anthropogenic carbon emissions on LAI in a probabilistic setting applying Causal Counterfactual Theory. The analysis prominently indicates the effects of climate change on many biomes - warming in northern ecosystems (greening) and rainfall anomalies in tropical biomes (browning). Our results do not support previously published accounts of dominant global-scale effects of CO2 fertilization. Altogether, our analysis reveals a slowing down of greening and strengthening of browning trends, particularly in the last two decades. Most models substantially underestimate the emerging vegetation browning, especially in the tropical rainforests. Leaf area loss in these productive ecosystems could be an early indicator of a slow-down in the terrestrial carbon sink. Models need to account for this effect to realize plausible climate projections of the 21st century. [ABSTRACT FROM AUTHOR]

A multiscale model for a colloid-polymer mixture is developed. The colloids are described as point particles interacting with each other and with the polymers with strongly repulsive potentials, while polymers interact with each other with a softer potential. The fluid in the suspension is taken into account by the multiparticle collision dynamics method (MPC). Considering a slit geometry where the suspension is confined between parallel repulsive walls, different possibilities for the hydrodynamic boundary conditions (b.c.) at the walls (slip versus stick) are treated. Quenching experiments are considered, where the system volume is suddenly reduced (keeping the density of the solvent fluid constant, while the colloid and polymer particle numbers are kept constant) and thus an initially homogeneous system is quenched deeply into the miscibility gap, where it is unstable. For various relative concentrations of colloids and polymers, the time evolution of the growing colloid-rich and polymer-rich domains are studied by molecular dynamics simulation, taking hydrodynamic effects mediated by the solvent into account via MPC. It is found that the domain size ℓd(t) grows with time t as ℓd(t) ∝ t1/3 for stick and (at late stages) as ℓd(t) ∝ t2/3 for slip b.c., while break-up of percolating structures can cause a transient 'arrest' of growth. While these findings apply for films that are 5-10 colloid diameters wide, for ultrathin films (1.5 colloid diameters wide) a regime with ℓd(t) ∝ t1/2 is also identified for rather shallow quenches. [ABSTRACT FROM AUTHOR]

In this paper, the accuracy of material decomposition (MD) using an energy discriminating photon counting detector was studied. An MD framework was established and validated using calcium hydroxyapatite (CaHA) inserts of known densities (50 mg/cm3, 100 mg/cm3, 250 mg/cm3, 400 mg/cm3), and diameters (1.2, 3.0, and 5.0 mm). These inserts were placed in a cardiac rod phantom that mimics a tissue equivalent heart and measured using an experimental photon counting detector cone beam computed tomography (PCD-CBCT) setup. The quantitative coronary calcium scores (density, mass, and volume) obtained from the MD framework were compared with the nominal values. In addition, three different calibration techniques, signal-to-equivalent thickness calibration (STC), polynomial correction (PC), and projected equivalent thickness calibration (PETC) were compared to investigate the effect of the calibration method on the quantitative values. The obtained MD estimates agreed well with the nominal values for density (mass) with mean absolute percent errors (MAPEs) 8 ± 11% (9 ± 15%) and 4 ± 6% (9 ± 14%) for STC and PETC calibration methods, respectively. PC displayed large MAPEs for density (27 ± 9%), and mass (25 ± 12%). Volume estimation resulted in large deviations between true and measured values with notable MAPEs for STC (40 ± 90%), PC (40 ± 80%), and PETC (40 ± 90%). The framework demonstrated the feasibility of quantitative CaHA mass and density scoring using PCD-CBCT. [ABSTRACT FROM AUTHOR]

Quantifying how the heart rate of ectothermic organisms responds to environmental conditions (e.g. water temperature) is important information to quantify their sensitivity to environmental change. Heart rate studies have typically been conducted in lab environments where fish are confined. However, commercially available implantable heart rate biologgers provide the opportunity to study free-swimming fish. Our study aimed to determine the applicability of an implantable device, typically used on fusiform-shaped fish (e.g. salmonids), for a perciform fish where morphology and anatomy prevent ventral incisions normally used on fusiform-shaped fish. We found that ventrolateral incisions allowed placement near the heart, but efficacy of the loggers was sensitive to their orientation and the positioning of the electrodes. Electrocardiogram detection, signal strength and subsequent heart rate readings were strongly influenced by logger orientation with a significant effect on the quality and quantity of heart rate recordings. We provide details on the surgical procedures and orientation to guide future heart rate biologger studies on perciform-shaped fish. [ABSTRACT FROM AUTHOR]

Hard-sphere fluids confined between parallel plates at a distance D apart are studied for a wide range of packing fractions including also the onset of crystallization, applying Monte Carlo simulation techniques and density functional theory. The walls repel the hard spheres (of diameter σ) with a Weeks-Chandler-Andersen (WCA) potential VWCA(z) = 4[variant_greek_epsilon][(σw/z)12 - (σw/z)6 + 1/4], with range σw = σ/2. We vary the strength [variant_greek_epsilon] over a wide range and the case of simple hard walls is also treated for comparison. By the variation of [variant_greek_epsilon] one can change both the surface excess packing fraction and the wall-fluid (γwf) and wall-crystal (γwc) surface free energies. Several different methods to extract γwf and γwc from Monte Carlo (MC) simulations are implemented, and their accuracy and efficiency is comparatively discussed. The density functional theory (DFT) using fundamental measure functionals is found to be quantitatively accurate over a wide range of packing fractions; small deviations between DFT and MC near the fluid to crystal transition need to be studied further. Our results on density profiles near soft walls could be useful to interpret corresponding experiments with suitable colloidal dispersions. [ABSTRACT FROM AUTHOR]

While otoliths are considered the most reliable structure to accurately age fish, a variety of otolith preparation techniques are available, which have consequences on the otolith's optical properties and therefore interpretation of growth bands. Recently, numerous studies from a variety of authors have criticised the use of whole otoliths in ageing sparids with sectioned otoliths subsequently acknowledged as the most reliable preparation technique. Despite this criticism; ageing data is still being generated from whole otoliths and other unreliable structures such as scales. In an attempt to understand the severity of this issue we conducted a global literature review of otolith preparation protocols used for sparids. We identified global spatial inconsistencies in otolith preparation techniques with some regions predominately using methods other than sectioned otoliths to age sparids. The review highlights the need for a standardisation of otolith preparation methods and a move towards the use of sectioned otoliths, or at least valid support where alternative structures or preparation techniques are used. Given that large numbers of studies have been conducted on whole otoliths in certain regions, it may be necessary to revaluate the existing growth parameters to ensure that accurate information is incorporated into management structures. [ABSTRACT FROM AUTHOR]

Objective: The number of students using prescription drugs to improve cognitive performance has increased within the last years. There is first evidence that the expectation to receive a performance-enhancing drug alone can result in improved perceived and actual cognitive performance, suggesting a substantial placebo effect. In addition, expecting a placebo can result in lower perceived and actual cognitive performance, suggesting a nocebo effect. Yet, the underlying mechanisms of these effects remain to be elucidated. The aim of our study was to investigate whether the expectation of receiving a performance-increasing drug or a performance-impairing drug leads to changes in actual and perceived cognitive performance, compared to a control group without expectation manipulation. Methods: A total of N = 75 healthy adults were recruited for an experiment to "try cognitive performance-modulating drugs." A participant's actual cognitive performance (alertness, working memory, sustained attention, and divided attention) using the standardized test of attentional performance (TAP) as well as their performance expectation were assessed. Participants were randomly assigned in equal numbers to either receiving a placebo performance increasing nasal spray ("Modafinil") or a nocebo performance impairing nasal spray ("Vividrin®") or no nasal spray (natural history). After placebo/nocebo nasal spray administration, cognitive performance was reassessed. Subsequent to the second assessment, participants rated their perceived change in cognitive performance, as well as adverse symptoms. Results: Unlike hypothesized, a positive or negative performance expectation did not result in changes in actual performance, corresponding to the induced expectation. Participants in the placebo-Modafinil group rated their perceived change in cognitive performance subsequent to the application of the nasal spray significantly better (d = 1.16) compared to the nocebo-Vividrin® group. Additionally, participants who expected to receive Modafinil felt less tired than participants in the Vividrin® group (d = 0.96). Conclusion: Manipulation of performance expectation affects the perceived change in performance and tiredness, but not the actual cognitive performance in healthy adults. This may explain why college students use such drugs despite their little impact on actual cognitive functioning. [ABSTRACT FROM AUTHOR]

Recent research on emergent constraints (ECs) has delivered promising results in narrowing down uncertainty in climate predictions. The method utilizes a measurable variable (predictor) from the recent historical past to obtain a constrained estimate of change in an entity of interest (predictand) at a potential future CO2 concentration (forcing) from multi-model projections. This procedure first critically depends on an accurate estimation of the predictor from observations and models and second on a robust relationship between inter-model variations in the predictor–predictand space. Here, we investigate issues related to these two themes in a carbon cycle case study using observed vegetation greening sensitivity to CO2 forcing as a predictor of change in photosynthesis (gross primary productivity, GPP) for a doubling of preindustrial CO2 concentration. Greening sensitivity is defined as changes in the annual maximum of green leaf area index (LAI max) per unit CO2 forcing realized through its radiative and fertilization effects. We first address the question of how to realistically characterize the predictor of a large area (e.g., greening sensitivity in the northern high-latitude region) from pixel-level data. This requires an investigation into uncertainties in the observational data source and an evaluation of the spatial and temporal variability in the predictor in both the data and model simulations. Second, the predictor–predictand relationship across the model ensemble depends on a strong coupling between the two variables, i.e., simultaneous changes in GPP and LAI max. This coupling depends in a complex manner on the magnitude (level), time rate of application (scenarios), and effects (radiative and/or fertilization) of CO2 forcing. We investigate how each one of these three aspects of forcing can affect the EC estimate of the predictand (Δ GPP). Our results show that uncertainties in the EC method primarily originate from a lack of predictor comparability between observations and models, the observational data source, and temporal variability of the predictor. The disagreement between models on the mechanistic behavior of the system under intensifying forcing limits the EC applicability. The discussed limitations and sources of uncertainty in the EC method go beyond carbon cycle research and are generally applicable in Earth system sciences. [ABSTRACT FROM AUTHOR]

Seasonality in photosynthetic activity is a critical component of seasonal carbon, water, and energy cycles in the Earth system. This characteristic is a consequence of plant's adaptive evolutionary processes to a given set of environmental conditions. Changing climate in northern lands (>30°N) alters the state of climatic constraints on plant growth, and therefore, changes in the seasonality and carbon accumulation are anticipated. However, how photosynthetic seasonality evolved to its current state, and what role climatic constraints and their variability played in this process and ultimately in carbon cycle is still poorly understood due to its complexity. Here, we take the "laws of minimum" as a basis and introduce a new framework where the timing (day of year) of peak photosynthetic activity (DOYPmax) acts as a proxy for plant's adaptive state to climatic constraints on its growth. Our analyses confirm that spatial variations in DOYPmax reflect spatial gradients in climatic constraints as well as seasonal maximum and total productivity. We find a widespread warming‐induced advance in DOYPmax (−1.66 ± 0.30 days/decade, p < 0.001) across northern lands, indicating a spatiotemporal dynamism of climatic constraints to plant growth. We show that the observed changes in DOYPmax are associated with an increase in total gross primary productivity through enhanced carbon assimilation early in the growing season, which leads to an earlier phase shift in land‐atmosphere carbon fluxes and an increase in their amplitude. Such changes are expected to continue in the future based on our analysis of earth system model projections. Our study provides a simplified, yet realistic framework based on first principles for the complex mechanisms by which various climatic factors constrain plant growth in northern ecosystems. [ABSTRACT FROM AUTHOR]

We studied the impact of boundary walls and inclusions to the charge collection efficiency (CCE) of cadmium telluride (CdTe) pad detectors. By using ion beam-induced charge (IBIC) analysis, we were able to locate and classify various defects. We used 2-MeV protons that have a penetration depth of about 40 $\mu \text{m}$ in bulk CdTe. The detectors we measured had a surface area of 1 cm $\times \,\, 1$ cm and a thickness of 1 mm. They were passivated either by using aluminum nitride (AlN) or by aluminum oxide (Al2O3) and had a mesh of titanium tungsten Schottky contacts on both anode and cathode sides. In this paper, we show the results of our measurements and analyze the potential impact of the found defects on the performance of CdTe detectors with Geant4 simulations. [ABSTRACT FROM AUTHOR]

Background Although the Fitkids Treadmill Test (FTT) has been validated and normative values are available for healthy 6- to 18-year-old children and adolescents, these facts do not automatically imply uptake of the test in routine practice of physical therapists. Objective The objectives of this study were to evaluate the utility of the FTT in different diagnostic groups and to explore potential factors affecting the use of the FTT in clinical practice. Design Mixed methods with both quantitative and qualitative data were used in this study. Methods Outcome parameters from the FTT were retrieved from the Fitkids database. For evaluation of the utility of the FTT, 2 indicators, exercise duration and maximal effort, were used. An online survey was sent to physical therapists in Fitkids practices to identify factors affecting the use of the FTT in clinical practice. Results The proportion of children and adolescents in each of the diagnostic groups who reached the minimal duration of a maximal exercise test ranged from 94% to 100%. The proportion of participants who reached a peak heart rate ≥180 beats/min ranged from 46% for participants with cognitive, psychological, or sensory disorders to 92% for participants with metabolic diseases. The most important facilitator for use of the FTT was the fact that most physical therapists were convinced of the additional value of the FTT. The main barriers were therapists' attitudes (resistance to change/lack of experience) and, on the environmental level, the absence of a treadmill ergometer in physical therapist practice. Limitations Structured interviews would have provided more information on potential factors affecting the use of the FTT in clinical practice. Conclusions This study has shown the clinical utility of the FTT in different diagnostic groups in pediatric physical therapist practice. Responding to the factors identified in this study should enable improved uptake of the FTT in clinical practice. [ABSTRACT FROM AUTHOR]

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To enhance understanding of Earth's climate, numerical experiments are performed contrasting a retrograde and prograde rotating Earth using the Max Planck Institute Earth system model. The experiments show that the sense of rotation has relatively little impact on the globally and zonally averaged energy budgets but leads to large shifts in continental climates, patterns of precipitation, and regions of deep water formation. Changes in the zonal asymmetries of the continental climates are expected given ideas developed more than a hundred years ago. Unexpected was, however, the switch in the character of the European–African climate with that of the Americas, with a drying of the former and a greening of the latter. Also unexpected was a shift in the storm track activity from the oceans to the land in the Northern Hemisphere. The different patterns of storms and changes in the direction of the trades influence fresh water transport, which may underpin the change of the role of the North Atlantic and the Pacific in terms of deep water formation, overturning and northward oceanic heat transport. These changes greatly influence northern hemispheric climate and atmospheric heat transport by eddies in ways that appear energetically consistent with a southward shift of the zonally and annually averaged tropical rain bands. Differences between the zonally averaged energy budget and the rain band shifts leave the door open, however, for an important role for stationary eddies in determining the position of tropical rains. Changes in ocean biogeochemistry largely follow shifts in ocean circulation, but the emergence of a "super" oxygen minimum zone in the Indian Ocean is not expected. The upwelling of phosphate-enriched and nitrate-depleted water provokes a dominance of cyanobacteria over bulk phytoplankton over vast areas – a phenomenon not observed in the prograde model. What would the climate of Earth look like if it would rotate in the reversed (retrograde) direction? Which of the characteristic climate patterns in the ocean, atmosphere, or land that are observed in a present-day climate are the result of the direction of Earth's rotation? Is, for example, the structure of the oceanic meridional overturning circulation (MOC) a consequence of the interplay of basin location and rotation direction? In experiments with the Max Planck Institute Earth system model (MPI-ESM), we investigate the effects of a retrograde rotation in all aspects of the climate system. The expected consequences of a retrograde rotation are reversals of the zonal wind and ocean circulation patterns. These changes are associated with major shifts in the temperature and precipitation patterns. For example, the temperature gradient between Europe and eastern Siberia is reversed, and the Sahara greens, while large parts of the Americas become deserts. Interestingly, the Intertropical Convergence Zone (ITCZ) shifts southward and the modeled double ITCZ in the Pacific changes to a single ITCZ, a result of zonal asymmetries in the structure of the tropical circulation. One of the most prominent non-trivial effects of a retrograde rotation is a collapse of the Atlantic MOC, while a strong overturning cell emerges in the Pacific. This clearly shows that the position of the MOC is not controlled by the sizes of the basins or by mountain chains splitting the continents in unequal runoff basins but by the location of the basins relative to the dominant wind directions. As a consequence of the changes in the ocean circulation, a "super" oxygen minimum zone develops in the Indian Ocean leading to upwelling of phosphate-enriched and nitrate-depleted water. These conditions provoke a dominance of cyanobacteria over bulk phytoplankton over vast areas, a phenomenon not observed in the prograde model. [ABSTRACT FROM AUTHOR]

Purpose: The discrepancy of continuously decreasing opportunities for clinical training and assessment and the increasing complexity of interventions in surgery has led to the development of different training and assessment options like anatomical models, computer-based simulators or cadaver trainings. However, trainees, following training, assessment and ultimately performing patient treatment, still face a steep learning curve.Methods: To address this problem for C-arm-based surgery, we introduce a realistic radiation-free simulation system that combines patient-based 3D printed anatomy and simulated X-ray imaging using a physical C-arm. To explore the fidelity and usefulness of the proposed mixed-reality system for training and assessment, we conducted a user study with six surgical experts performing a facet joint injection on the simulator.Results: In a technical evaluation, we show that our system simulates X-ray images accurately with an RMSE of 1.85 mm compared to real X-ray imaging. The participants expressed agreement with the overall realism of the simulation, the usefulness of the system for assessment and strong agreement with the usefulness of such a mixed-reality system for training of novices and experts. In a quantitative analysis, we furthermore evaluated the suitability of the system for the assessment of surgical skills and gather preliminary evidence for validity.Conclusion: The proposed mixed-reality simulation system facilitates a transition to C-arm-based surgery and has the potential to complement or even replace large parts of cadaver training, to provide a safe assessment environment and to reduce the risk for errors when proceeding to patient treatment. We propose an assessment concept and outline the steps necessary to expand the system into a test instrument that provides reliable and justified assessments scores indicative of surgical proficiency with sufficient evidence for validity. [ABSTRACT FROM AUTHOR]

Recent research on Emergent Constraints (EC) has delivered promising results. The method utilizes a measurable variable (predictor) from the recent historical past to obtain a constrained estimate of change in a difficult-to-measure variable (predictand) at a potential future CO2 concentration (forcing) from multi-model projections. This procedure critically depends on, first, accurate estimation of the predictor from observations and models, and second, on a robust relationship between inter-model variations in the predictor-predictand space. We investigate issues related to these two themes in this article, using vegetation greening sensitivity to CO2 forcing during the satellite era as a predictor of change in Gross Primary Productivity (GPP) of the Northern High Latitudes region (60° N-90° N, NHL) for a doubling of pre-industrial CO2 concentration in the atmosphere. Greening sensitivity is defined as changes in annual maximum of green leaf area index (LAImax) per unit CO2 forcing realized through its radiative and fertilization effects. We first address the question of how to realistically characterize the greening sensitivity of a large area, the NHL, from pixel-level LAImax data. This requires an investigation into uncertainties in LAImax data source and an evaluation of the spatial and temporal variability in greening sensitivity to forcing in both the data and model simulations. Second, the relationship between greening sensitivity and ΔGPP across the model ensemble depends on a strong coupling among simultaneous changes in GPP and LAImax. This coupling depends in a complex manner on the magnitude (level), time-rate of application (scenarios) and effects (radiative and/or fertilization) of CO2 forcing. We investigate how each one of these three aspects of forcing can impair the EC estimate of the predictand (ΔGPP). Accounting for uncertainties in greening sensitivity and stability of the relation between inter-model variations results in a quantitative estimate of the uncertainty (±0.2 Pg C yr-1) on constrained GPP enhancement (ΔGPP = +3.4 Pg C yr-1) for a doubling of pre-industrial atmospheric CO2 concentration in NHL. This ΔGPP is 60% larger than the conventionally used average of model projections. The illustrated sources of uncertainty and limitations of the EC method go beyond carbon cycle research and are generally relevant for Earth system sciences. [ABSTRACT FROM AUTHOR]

Copyright of Der Unfallchirurg is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)