The dynamics of protein structures and their interactions are responsible for many cellular processes. The rearrangements and interactions of proteins, which are often transient, occur in solution and may require a biological environment that is difficult to maintain in traditional structural biological approaches. Quantitative crosslinking/mass spectrometry (QCLMS) has emerged as an excellent method to fill this gap. Numerous recent applications of the technique have demonstrated that protein dynamics can now be studied in solution at sufficient resolution to gain valuable biological insights, suggesting that extending these investigations to native environments is possible. These breakthroughs have been based on the maturation of CLMS at large, and its recent fusion with quantitative proteomics. We provide here an overview of the current state of the technique, the available workflows and their applications, and remaining challenges. In-solution dynamics of protein structures and their interactions can be studied by QCLMS.Successful applications of QCLMS provide insights into multiple different biological processes.Recent advances in QCLMS allow analyses in the context of native cellular environments, including living cells.Alternative workflows allow researchers to tailor the analysis to their biological question.Progress in data processing now offers this technique to researchers with limited initial expertise in crosslinking and quantitative proteomics. Recent progress in QCLMS  is now changing this.Molecular switches govern the assembly of protein complexes and regulate the activity of networks through irreversible domain rearrangements. Other cellular processes are controlled by alterations in interconversion rates and the relative populations of conformational ensembles of proteins. Irrespective of their exact nature, protein dynamics are influenced by their cellular environment and thus are largely intractable to traditional structural biology methods. Several novel tools are available to assist in this regard. In-cell NMR As part of a crosslinking analysis, proteins react with added crosslinkers under physiological conditions, which include environments such as inside living cells. The crosslinker has two reactive ends, either of which can react with an amino acid residue at the surface of a protein. A pair of residues must be sufficiently close in space to be linked. Crosslinked residue pairs (crosslinks) are then detected by MS in the form of crosslinked peptide pairs following proteolytic cleavage of the protein. Identifying the links thus provides useful information on the 3D fold of a protein; however, the resulting model is static Box 1Crosslinking complex protein mixtures has two fundamental problems. Crosslinked peptides are difficult to detect among the many linear peptides, exacerbated by their generally low abundance, leading to poor signal patterns Quantitation of crosslinked peptides suffers from their generally low signal intensity. This is a consequence of the many different crosslinks that can form from any reactive residue. Furthermore, crosslinking is rarely performed to completion to avoid proteins becoming indigestible by proteases. Low signal intensity leads to problems in analysis: elution peaks contain few datapoints, isotope envelopes are incomplete, and signal/noise ratios tend to be low. Automatic peak-integration algorithms deal with such signals only with difficulty, and signal variation in replicated analysis tends to be high. Targeted analysis can improve the sensitivity and accuracy of quantitation, albeit by limiting the scale of the analysis. Using DIA methods can improve the throughput of targeted analysis. Nevertheless, the dynamic range defined by the high signal intensity of some linear peptides and the low signal intensity of many crosslinked peptides remains a challenge that may only be addressed by improving enrichment methods. It should be noted that low signal intensity also affects isobaric labelling approaches because low precursor abundance also leads to low intensity or lack of reporter ions, resulting in inaccuracy and failure in quantitation.Quantitative crosslinking observes changes in crosslinked residue pairs (crosslinks) that tend to be supported by few or even single MS features. This is disadvantageous because individual observations can be affected by artefacts that occur during sample processing  and MS analysis , which reduce the reproducibility of measurements. Quantitative proteomics bases its statements of protein expression changes on independent data of multiple peptides for each protein. Likewise, QCLMS should group evidence by combining multiple crosslinks for structural interpretation.Our current understanding of how crosslink signal changes relate to the underlying protein conformational changes is based on only a few studies. It remains to be seen in broader studies how conformational changes affect crosslink yields in quantitative detail, and how crosslink changes can consequently guide structural modelling.Alt-text: Box 1Initially, QCLMS was limited by the typically small number of crosslinks detected in CLMS experiments, as well as by the difficulty of quantifying them, because general quantitative proteomics software was not able to take crosslinked peptides into consideration. These restrictions have been largely overcome owing to recent progress, as outlined below. Consequently, following early work proving the concepts of QCLMS There are six key elements that all QCLMS workflows have in common. These are crosslinking, digestion, enrichment of crosslinked peptides, liquid chromatography–tandem MS (LC–MS/MS) analysis, identification, and quantitation of crosslinked peptides A.Figure For label-free quantitation, different protein states are analysed as individual samples A. In theLabel-free quantitation is compatible with any crosslinking chemistry. However, it is worth noting that, in some circumstances, different protein states may only be separated after the crosslinking reaction, for example by SDS-PAGE data-dependent acquisition ’ B. This i2H, 13C, 15N, and 18O)  A. The prCurrently, isotope-labelled crosslinkers et al. benefitted from this method because they were able to distinguish whether an observed crosslink change was likely due to conformational changes or simply the result of protein abundance changes SILAC (stable isotope-labelled amino acids) et al.218O, which added 8 Da to all crosslinked peptides relative to digestion in normal water. The two digests were then mixed 1:1 for LC–MS/MS. One should be aware that the carbonyl oxygen exchange reaction can be difficult to optimise, which results in peptides with varying levels of incorporation and complicates the quantitation In both labelling approaches, the crosslinked protein states are mixed in a 1:1 ratio, either before or after digestion. Alluding to a third strategy, Huang After mixing the protein states, enrichment steps, and LC–MS/MS analysis, the identification and quantitation of crosslinked peptides are conducted according to the same procedure as described in the label-free workflow. Importantly, to achieve accurate quantitation, a replicate analysis with label-swapping is required when using this procedure Within a same acquisition, isotope-labelled quantitation compares the MS signals of crosslinked peptides which are derived from different protein states B. This aAlternatively, quantitation can be achieved using an isobaric labelling reagent set such as iTRAQ et al.Yu m/z). Such a targeted approach confirmed the previous in vitro finding that the centres of the coiled-coils in the chromatin-resident condensin complex can also approach one another closely in situ in mitotic chromosomes parallel reaction monitoring (PRM) The three discovery-type workflows described above allow crosslinked peptides to be identified and quantitated in a single experiment. However, the accuracy of quantitation is challenged by the generally low signal intensities of crosslinked peptides . A solutdata-independent acquisition  absolute quantitation using synthetic standards Crosslinks not only provide information on protein folding and interactions, but they also reveal regions that exhibit structural differences between conformational states. The signal fold-changes and appearance/disappearance of crosslinks in these regions allow researchers to create schematic models that elucidate the dynamic aspects of their protein systems Outstanding QuestionsHow can the ambiguous information of crosslink changes  be disambiguated?How can superimposed data of different conformers be deconvoluted?How can small structural changes be detected?What value of fold-change reliably indicates a structural change?How can fold-change data be used in modelling?What timescales can be captured? When does the speed of a process supersede the speed of the chemical reaction?What is the influence of crosslinking on structural dynamics? Does crosslinking lead to shifted equilibria between multiple conformational states?What density of crosslink data is needed to reliably call a protein structure unchanged? When does absence of signal become informative?What influences the yield of crosslinking? Can the MS intensity of different crosslinks be used to generate structural insights?CLMS has become a standard component of integrated structural biological studies. Fusing this tool with quantitative proteomics can animate the (often) static images of protein structures generated by established technologies. QCLMS has now left the corner of method developers and benchmark studies. Although some technological challenges remain to be addressed (see Outstanding Questions), the wide set of successful applications attest to for the current breadth of research questions that can be approached. This will no doubt further expand as the technology progresses and as more application areas are explored. Importantly, QCLMS is not limited to isolated proteins or protein complexes. Screening protein structural and interaction dynamics in the context of the whole proteome is becoming possible. Therefore, both structural biology and systems biology will benefit from QCLMS, possibly assisting their fusion into structural systems biology.


Informed management of livestock on rangelands underpins both the livelihoods of communities that depend on livestock for sustenance, and the conservation of wildlife that often depend on livestock-dominated landscapes for habitat. Understanding spatial patterns of rangeland productivity is therefore crucial to designing global development strategies that balance social and environmental benefits. Here we introduce a new rangeland production model that dynamically links the Century ecosystem model with a basic ruminant diet selection and physiology model. With lightweight input data requirements that can be met with global sources, the model estimates the viability of broad livestock management decisions, and suggests possible implications of these management decisions for grazing wildlife. Using minimal field data, the new rangeland production model enables the reliable estimation of cattle stocking density; this is an important predictor of the viability of livestock production and forage available for grazing wildlife. Rangelands also provide critical habitat for wildlife, especially in countries like Kenya where protected areas contain a minority of the country’s wildlife2. Overgrazing of rangelands worldwide by domestic livestock is linked to erosion, desertification, and habitat degradation for the many other species that thrive on healthy grasslands3. Sustainable management of the world’s rangelands is therefore crucial to balancing human well-being with ecosystem health and wildlife conservation, but we lack accessible tools for exploring how changes in management could affect social and environmental benefits.Rangelands cover 30% of the earth’s ice-free surface and provide nourishment for 800 million people suffering from food insecurity4. Non-equilibrium theory predicts that variability in precipitation, in addition to total rainfall, strongly limits rangeland productivity5, and retrospective analyses support this prediction6. Understanding how changing rainfall amount and distribution will impact livestock productivity in the future is crucial to climate change adaptation.Critical uncertainties also exist about productivity on the world’s rangelands under climate change. As the global climate shifts, rainfall patterns are expected to change both in total amount and in variability7, and are a well-established means of assessing management strategies that balance consumption of forage resources with efficient production of livestock9. Most currently available livestock production models, however, fall on one of two ends of a spectrum of system scale and data requirements10. For example, detailed, management-oriented models such as SPUR11 and IFSM12, while very powerful tools in contrasting ranch-scale management scenarios, require intensive parameterization and are often designed for use in a restricted geography . At the opposite extreme are continental (e.g.13) or global (e.g.15) rangeland models. These tools, while useful for monitoring and large-scale forecasting, are limited in the level of detail with which management scenarios can be compared.Rangeland ecosystem models are a vital tool for forecasting and understanding productivity changes under climate change16. Here we use an array of properties managed for cattle production and wildlife conservation in the semi-arid savanna of Laikipia, Kenya as a case study to examine the viability of livestock production and forage for wildlife under variable precipitation scenarios and at different livestock densities. Laikipia is a large (9666 km2) region that spans a wide range of rainfall and productivity; there, livestock ranching and wildlife-related tourism are of primary economic importance17.Here we introduce a new approach that bridges these two extremes: a dynamic model that preserves much of the complexity of management-oriented models while addressing grazer-forage interactions at a landscape scale. We have designed the model to rely on global data sources and to be readily applicable to decision contexts around the world, similar to the ecosystem services models in the InVEST model suiteWe used field data describing biomass and animal densities in Laikipia (data available from Mendeley Data: 10.17632/4m3xybvdb6.2) to test the new model’s ability to address three key questions: first, can livestock management patterns, which are often unknown at landscape scales, be estimated from minimal field measurements? Second, what are the impacts of magnitude and variability of precipitation on the viability of livestock production? And finally, after accounting for forage offtake by livestock, what is the likely range of grazing wildlife densities that may also be supported under extreme climate and management scenarios?A brief model description is included here; see Supplementary Appendix 18) with a basic ruminant physiology model adapted from GRAZPLAN19 to estimate the potential of a given site to meet the forage requirements of ruminant livestock and to support populations of grazing wildlife  is to project forage production and diet sufficiency for grazing animals under different conditions of climate and management. The structure of this model couples the Century ecosystem model , the model simulates a management decision to halt grazing at the site when the threshold is reached. In reality, the management choices available in cases of very low forage availability are highly manager- and context-specific: some managers would address such a shortfall in available forage by relocating their animals to a site with greater forage availability, while others may choose to import supplemental feed. In the Rangeland model, a shortfall in forage intake restricted by the management threshold is simply reflected by a low diet sufficiency value, indicating that available forage at the simulated site did not meet the animals’ energetic needs.Note that restriction of forage intake by the management threshold is distinct from, and operates in addition to, the predicted behavioral and physiological response of livestock to forage quantity and quality  where each grazing event is described by its intensity level (percent biomass removed by herbivores). However, this information is seldom known across landscapes. For this reason, we use a new method to “back-calculate” the management, developing a routine that calibrates the management schedule based on empirical measurement of biomass.back-calculate management routine to set this historical management schedule, based on current observed conditions. While this method assumes that current management is reflective of historic management, in absence of other information to the contrary, it is the simplest way to initialize the Century model.In particular, Century requires a “spin-up” period to establish initial soil nutrient pools, and a management schedule must be supplied for this period. If it is difficult to derive a current or recent management schedule in many natural grasslands, reconstructing a long-term management history of such areas in the detail that the model requires is even less plausible. Therefore, we use the back-calculate management routine launches the Rangeland model with a starting simulated grazing schedule, compares simulated biomass to an empirical measurement, and iteratively modifies the simulated grazing schedule until empirical biomass is matched. The routine modifies the grazing schedule either by adding or removing grazing events, modifying the intensity of grazing events , or both. If the user specifies that both the schedule and intensity should be modified, the routine first modifies the schedule until no more opportunities exist to do so, and then modifies intensity. For example, if simulated biomass is higher than empirical biomass, the routine adds grazing events prior to the empirical measurement date. If grazing events are added to every month within the maximum amount of time allowable but simulated biomass is still too high, the routine then increases grazing intensity. Intensity is modified at each iterative step of the routine until empirical biomass is matched or the maximum allowable iterations are completed.The 22 for Century23; and references therein for GRAZPLAN). In the following sections, we describe testing and application of the Rangeland model to a grassland case study in Kenya.By coupling two existing models , the Rangeland model is intended to be readily applied to new sites that span the wide range of environments where Century and GRAZPLAN have been used and validated , where livestock are grazed in small herds by herders and kept in moveable bomas or paddocks at night. We ran a separate simulation at each of eight weather stations , using a single empirical biomass measurement taken near that site as the biomass target that the routine should match . We evaluated any systematic bias of the model in estimating biomass from recent grazing management with the mean bias (Eq. (24).Oi is the ith observed biomass value; Pi,default and Pi,calc are the ith simulated biomass values using the default and the back-calculated management schedule, respectively; and n is the total number of biomass measurements.We tested the ability of the SEP, Eq. 24). We eias (Eq. 24).2\docWe calculated modeled grazing intensity for each site as the average monthly biomass removed by herbivores over the 12 months preceding the final empirical biomass measurement. Empirical animal density surrounding each site was calculated as the average density of dung per transect across transects within the 1600 ha surrounding the site, spanning up to 13 months of field data collection. We classified animals into broad functional groups according to their feeding strategy, and compared the density of each functional group to modeled grazing intensity with simple linear correlation.Following our test of the Rangeland model at the local scale on OPC, we extended our application of the model to the regional scale to contrast viable levels of livestock production across 25 properties in Laikipia . See Supplementary Appendix We ran the model for 24 months following the spin-up period, at a series of stocking density levels spanning the range of reported densities in the region  to compare intra-annual precipitation variability between precipitation scenarios. The PCI is calculated as in Eq. .Manipulated precipitation inputs were used to drive the “spin-up” simulations in addition to the experimental simulations that lasted for 24 months. In all cases, precipitation did not vary between years of the spin-up simulation or the experimental period - only intra-annual precipitation was varied. We analyzed the relationship of maximum viable density to site and precipitation factors with multiple linear regression, where the response variable was maximum viable density for a given property and precipitation scenario. Predictor variables were the property, total precipitation, PCI, and a two-way interaction including total precipitation and PCI.per se. The index is a meaningful relative metric of additional grazing animals that could be supported by available forage after cattle offtake; the index is expressed as cattle density and is not directly interpretable as number of any specific wildlife species.We estimated the potential for these properties across Laikipia to support wild grazers after offtake of forage by cattle, given a certain precipitation regime and cattle stocking density, with a simplified index: viable wild grazer density. We calculated viable wild grazer density as the difference between maximum viable cattle density, as estimated by the Rangeland model, and reported cattle density. See Supplementary Table et al.17 as primarily dominated by livestock, by wildlife, or by integration of livestock with wildlife.We first calculated viable wild grazer density on each property under current livestock stocking density by comparing the maximum viable livestock density to the reported livestock density on that property. We characterized the apparent effect of property management strategy on viable wild grazer density by contrasting the proportion of viable density available for wildlife  among properties classified by Keesing 26.We then contrasted impacts of extreme regional changes in climate and livestock management on projected viable wild grazer density by repeating the analysis under two climate scenarios and two management scenarios. We calculated the change in viable wild grazer density on each property that would be expected under extreme drought conditions , and in conditions of heavily increased rainfall , assuming that the livestock density at each property remained constant. These extremes define the range of total rainfall observed in Laikipia between 1984 and 2014, and are reasonable bounds for climate extremes in the future17.To calculate the potential change in wildlife habitat under region-wide management changes, and to reveal how management extremes compare to climate extremes in terms of their potential to impact wildlife, we estimated viable wild grazer densities under a scenario where each property in the region switched to the lowest regional reported livestock density (0.07 cattle/ha), and to the highest regional reported livestock density . These two scenarios therefore describe the range of consequences for regional wildlife habitat if all managers were to switch to a “wildlife-dominated” management strategy, or to a “livestock-dominated” strategyback-calculate management routine successfully matched target biomass for all eight sites corresponding to weather stations on OPC, and greatly improved the goodness of fit to empirically measured biomass relative to the default management history for northern Kenya .Multiple regression analysis of the maximum viable livestock density under manipulated precipitation scenarios demonstrated that precipitation variability, in addition to total rainfall, has significant impacts on livestock production viability. As expected, total precipitation was a strong positive predictor of maximum viable density . Both PCI  and site  were non-significant as main effects. PCI interacted strongly with precipitation, however , limiting the positive effect of precipitation on viable density when intra-annual variability was high  showed a higher utilization rate of the space by livestock–that is, reported livestock densities made up a larger percentage of the maximum viable density on those properties compared to other properties , yet has been limited in its applicability in rangelands because it is so difficult to know the density of herbivores in many systems a priori. By coupling Century with a basic herbivore physiology model, we are able to examine trends in forage availability and reliability across a heterogeneous landscape and under variable climate.We present here a new coupled model with lightweight data requirements that can be used to investigate management scenarios on rangelands that are typically out of reach in low-data environments or at the larger spatial extents of many conservation contexts. The Century model has been tested in its ability to predict grass growth around the world, including in response to herbivore pressure  by more than 1,188,000 kg/ha over simulations using preset grazing schedules from nearby regions. Indeed, calibrating the model in this way changed those starting assumptions by 31–230%, from removing all scheduled grazing events to adding 18 months of grazing events to the 2-year calibrated period, across locally heterogeneous sites. The small sample size of sites did not allow us to assess the uncertainty associated with this improvement in MSEP, for example through cross-validation27. Correlations with empirically-estimated cattle dung density, however, showed that the routine accurately differentiated between sites with different levels of grazing pressure. This is an important advance because grazing intensity impacts both within-season dynamics, such as phenology and forage nutritive quality, and longer-term effects via soil carbon and nutrient cycling28.The 29). This more closely matches the scale of policies or incentives made by many decision-makers, and will enable prioritization of management interventions at that scale.The approach described here lifts a critical data barrier in characterizing past site management. While information to describe recent grazing pressure is crucial to understanding and forecasting site dynamics, it is often proprietary and is typically available only at coarse administrative scales that may contain heterogeneous management practices. The method of estimating grazing intensity demonstrated here also suggests the possibility of using remote sensing of biomass to calibrate ecosystem models at landscape and larger scales  suggest that absolute values predicted by the model should be interpreted cautiously. Predictions by Century of the growth of ungrazed biomass were positively, but nonsignificantly, correlated with empirical grass growth  were much higher than most reported densities in the region, where the mean reported stocking density was only 0.25 animals/ha37 and that human-wildlife conflict is exacerbated by environmental stress38. Increasingly severe drought cycles are considered one of the major drivers of ongoing declines in wildlife populations in East Africa2. Although current forage resources in Laikipia are sufficient to support co-management of domestic livestock with wildlife, increasingly frequent and extreme drought conditions may pose the greatest impediment in the future to livestock-wildlife integration. Conversely, these results also suggest that the more mesic, productive properties in the region may have ample forage resources to support additional grazers, and should therefore see an opportunity to reap potential ecological and economic benefits of integrating domestic livestock with wildlife39.Climate was also the primary driver of our simplified metric for wildlife habitat, describing the density of wild grazers that could be supported by forage after cattle offtake: our extreme climate and management scenarios suggest that within the bounds of observed conditions in the region, climatic conditions during any particular time period limit available resources for wildlife much more strongly than competition for forage with livestock. Historical evidence supports that grazing wildlife and livestock are more likely to compete during periods of drought40. Nor can the model represent spatiotemporal niche partitioning between livestock and wildlife that may reduce competition within large and heterogeneous properties41, or potential impacts of domestic livestock and their herders on the distribution of real or perceived predation risk for wildlife42. Spatial interactions are an important dimension of livestock-wildlife coexistence in the Kenyan landscape in particular43, where habitat selection by wildlife is often related to current or recent presence of grazing livestock41. However, in regions like Laikipia where fragmentation of the landscape increasingly limits the ability of wildlife to move adaptively in response to large-scale resource heterogeneity, including heterogeneity created by livestock, competitive interactions with livestock may dominate44. The possibility of extending the Rangeland model to a gridded, rather than point-based, implementation is a promising direction for the future; this extension of the model would allow for more explicit consideration of the role of animal movement and spatial segregation in livestock-wildlife coexistence.Because the Rangeland model is point-based, predicting forage growth and consumption by herbivores at a single location, it is limited in its ability to model spatial interactions between livestock and wildlife. The model cannot in its present form, for example, account for facilitation of highly nutritious forage for wildlife following abandonment of cattle bomas45. Further, because encroachment of woody plants into grassy habitats may limit forage availability for cattle46, the potential role of browsing wildlife in suppressing woody encroachment40 is another important dimension of interactions between livestock and wildlife that is not addressed by the model.The Rangeland model is also restricted to representing herbaceous biomass only, and does not currently predict the growth or consumption of shrubs or trees. The model is therefore unsuitable for simulation of browsing or mixed-feeder herbivores, which constitute a large portion of wildlife in Laikipia17. While parameters to apply the ruminant submodel to sheep are readily available from GRAZPLAN19, it would be incorrect to apply the model in its current form to predict the viability of goat production in areas where woody vegetation provides a significant source of forage. This is an important consideration, particularly as the abundance of shoats in Laikipia is increasing47.The constraint that the model simulates only herbaceous vegetation also limits its ability to represent all domestic livestock production in Laikipia. On a few of the properties surveyed in Laikipia, sheep and goats (identified collectively as “shoats” because their dung could not be reliably differentiated) represented a majority of livestock dung, although the median value was only 36%48. The rainfall manipulation experiment showed that high intra-annual variability of precipitation limited the viable grazer density in addition to total rainfall (Fig. 49. Given that Century does not simulate changes in plant community composition, the Rangeland model is not applicable on its own to simulate grazing-induced transitions between multiple steady states50, though it could evaluate differences between those states if coupled with a state and transition model.The application of the Rangeland model described here assumes relatively steady-state patterns of productivity across properties within Laikipia, driven by our modeling choice to use average annual precipitation as model inputs on regional properties. Rather than interpreting the metric of maximum viable density of livestock as carrying capacity, which would presume that an equilibrium was established, however, our simulations using experimentally manipulated precipitation scenarios bridge the model conceptually with non-equilibrium theoryall Fig. , and sho51. There is great potential to model process-based linkages between viable livestock production and other ecosystem processes that can be modeled with Century, including carbon and nutrient cycling, which could then be used as drivers to link grazing dynamics with other ecosystem services models. By linking Century with a ruminant physiology model, it is also likely that the model could address greenhouse gas emissions from livestock in a manner that is fully integrated with other ecosystem services. Because the characteristics of ruminant metabolism driving methane emissions from livestock are highly sensitive to forage protein content and digestibility52, we caution that any application of the Rangeland model addressing greenhouse gas emissions must carefully validate the plant parameterization controlling these characteristics.This application of the Rangeland model in Laikipia focuses on the primary model outputs of forage availability for grazing livestock and wildlife. Given the range of ecosystem-level quantities simulated by Century, however, the model clearly has potential to be useful in many other contexts. For example, Century’s detailed representation of soil carbon suggests that the model can be useful in predicting grazing dynamics in grasslands recently converted from forest or other vegetation typesThe challenge as well as the importance of understanding temporal variability and spatial heterogeneity grows as we work across larger scales – and landscape or regional level information is what most conservation decisions demand. Any form of land management in grasslands will often have to contend with livestock operations, and understanding the impact of grazing in concert with climate variability across large and spatially heterogeneous regions requires a new approach to rangeland modeling. The model described here, which can help to tease apart the interaction between total precipitation, precipitation variability, and grazing intensity, across space and time, can help to better forecast rangeland dynamics under simultaneous pressures from changing climate and management. By integrating Century’s ecosystem flows with a basic ruminant diet selection and physiology submodel, the Rangeland model extends the capability of Century beyond its historical applications, adding dynamic diet selection and productivity of livestock to the purview of Century’s whole-ecosystem approach. This new modeling approach provides an opportunity to include the impacts of grazing on rangelands and the benefits we derive from these systems, across heterogeneous landscapes and for a wide variety of decision contexts.Supplemental Information


The genetic similarity to humans of nonhuman primates makes them well suited to serve as models of complex human disorders such as alcoholism. Like humans, nonhuman primates vary with respect to their alcohol consumption, even within the same species. Studies of the origins of high consumption among nonhuman primates have suggested that both genetic and environmental factors play a role in their drinking. In fact, researchers have found some support for multiple subtypes of alcoholism among nonhuman primates. Because of their close genetic similarity to humans and their complex social behaviors, nonhuman primates have been widely used to study a number of psychiatric syndromes , although at least one study investigated squirrel monkeys, a New World species that frequently is used in the laboratory . AlthougResearchers studying alcohol consumption in nonhuman primates have made significant progress in developing an animal model of alcohol abuseResearchers investigating alcohol problems now widely recognize that alcohol abuse and alcoholism have multiple etiologies . Thus, cStudies of the factors that affect drinking rates in nonhuman primates often involve comparing subjects exposed to stressful environments during infancy with those raised under normal laboratory conditions. For example, rhesus macaques that spend the first 6 months of their lives only in the constant company of their age peers  and are chronically absent from their mothers behave differently, displaying less curiosity and more fear, than those raised by their mothers . In adolescence and in adulthood, drinking rates in the two groups can be compared during additional stress-provoking challenges, such as separating subjects from other monkeys for certain periods of time  . The macStudies using adolescent rhesus macaques have shown that during nonstressful periods, such as routine life in subjects’ home environments, individual rates of alcohol consumption are positively correlated with characteristics of anxiety and fearfulness among animals . Such chNevertheless, not all mother-reared macaques increase their alcohol consumption rates in response to such stressors, and some peer-reared subjects do not develop high consumption rates following their early rearing experiences. These animals also display lower levels of anxiety, and their moderate responses may result from genetic predispositions. Within both the peer- and the mother-reared groups, individual differences in alcohol consumption are directly related to behavioral measures of anxiety, such as clasping themselves or behaviorally withdrawing, and biological measures, such as levels of plasma corticotropin, a hormone secreted by the pituitary gland that increases during periods of most types of stress. These findings suggest that increased levels of anxiety, whether resulting from prior early rearing experiences or from temporary challenges such as social separation, can increase alcohol consumption. These results are similar to those observed in Cloninger’s type I alcoholism .Higley and colleagues recently have begun a series of studies designed to investigate parallels between Cloninger’s type II alcoholism and correlates of high alcohol consumption in adolescent and young adult rhesus macaques living in groups. These studies have been reported in preliminary form .The findings show that high rates of alcohol consumption are observed in subjects with infrequent social interactions, less competent social behaviors, and high rates of aggression. These subjects are violent, impulsive, and eventually ostracized by their peers . Their aImpaired serotonin function also results from genetic background , and somWith some exceptions, these findings generally are consistent with predictions from Cloninger’s type II model of excessive alcohol consumption in men, among whom Cloninger has found low CSF 5–HIAA levels a,b. BecaResults of the type I and type II studies using nonhuman primates have hinted that genetic influences play a role in rates of alcohol consumption. Emerging evidence from nonhuman primate sibling studies also suggests a genetic influence on alcohol consumption in rhesus macaques. Rates of alcohol consumption were studied in 55 paternal half-sibling adolescent and young adult subjects that were reared apart from their fathers. Although the results are preliminary because of the small sample size, more than 50 percent of the variation between subjects in rates of alcohol consumption appeared to result from paternal genetic contributions, suggesting that genetic factors greatly influence alcohol intake rates. Although nonhuman primates with high rates of alcohol consumption have been shown to exhibit aspects of type I and type II alcoholism, a number of features of alcoholism are yet to be demonstrated adequately in these animals to produce a model for all aspects of alcoholism. For example, in contrast to human data , Higley A phenotype for high rates of alcohol consumption is present in at least some species of nonhuman primates. Subjects that exhibit this behavior will consume alcohol at rates producing intoxication. Adverse experiences early in life appear to exaggerate genetic predispositions for alcohol consumption. Underlying etiological mechanisms and biobehavioral correlates of the high-consumption phenotype—such as stress, anxiety, antisocial-like behaviors, and impaired serotonin functioning—parallel many of the predictions of Cloninger’s neurogenetic model of alcoholism.These examples from nonhuman primate research demonstrate its value for studying aspects of human alcoholism. Thus, it is surprising that nonhuman primates have been used so infrequently in this field. Their use in alcohol consumption research is a relatively recent development. Fewer than 100 research papers on this topic have been published during the past 25 years, compared with the more than 380 articles published over the past 3 years alone on alcohol consumption in mice and rats. Because humans and other primates share a large percentage of their genetic material , studies


A primary goal in tissue engineering is to develop functional tissues by recapitulating salient features of complex biological systems that exhibit a diverse range of physical forces. Induced pluripotent stem cells (iPSCs) are promising autologous cell sources to execute these developmental programs and their functions; however, cells require an extracellular environment where they will sense and respond to mechanical forces. Thus, understanding the biophysical relationships between stem cells and their extracellular environments will improve the ability to design complex biological systems through tissue engineering. This article first describes how the mechanical properties of the environment are important determinants of developmental processes, and then further details how biomaterials can be designed to precisely control the mechanics of cell-matrix interactions in order to study and define their reprogramming, self-renewal, differentiation, and morphogenesis. Finally, a perspective is presented on how insights from the mechanics of cell-matrix interactions can be leveraged to control pluripotent stem cells for tissue engineering applications. Complex biological structures arise from developmental processes, such as morphogenesis, which involves the precise transformation of materials with diverse physical properties. These materials consist of extracellular matrix (ECM), interstitial fluids (IFs) within the ECM, and cells  toward various cell fates and tissue types. This article aims to describe the relationship between biomaterial mechanics and stem cell functions while summarizing existing strategies for biomaterials to direct stem cell self-renewal, reprogramming, and differentiation. Then, the article presents a perspective on the future of biomaterial systems to implement biomaterial mechanics in iPSC applications for tissue engineering.Pluripotent stem cells (PSCs) are unique in their potential to undergo self-renewal and differentiation into many distinct types of cells. While ESCs were previously the major source of PSCs  undergo osteogenesis at an optimal stiffness  that guide the patterned development of tissues, but also on precise control of physical forces. Remarkably, studies in the past decade show that some stem cells can spontaneously develop into organoids when they are cultured in animal-derived matrices such as Matrigel and collagen while in the presence of a specific combination of growth factors Clevers, . In partMechanical properties of the nucleus are closely linked to chromatin states (Wang P. et al., Since optimal mechanical properties of biomaterials will depend on different cell types and how cells are eventually organized in different tissues, solutions beyond simple bulk cell-matrix interactions will likely be required to leverage biomaterial mechanics for tissue engineering with iPSCs. From a manufacturing perspective, developing methods to precisely control biomaterial mechanics in a spatiotemporal manner using a consistent base material will lead to an economical and elegant solution to fabricate autologous tissues consisting of multiple cell types from a single iPSC source. One general strategy to achieve this goal is to combine biomaterial design with instrumentation, such as by using printers. An important advantage of this strategy is that extrinsic and intrinsic material properties can be independently controlled. In particular, light-crosslinkable materials, such as hydrogels with a methacrylate group, have been used to spatially control hydrogel crosslinking at the microscale and pattern different ligands or cells within the same substrate (Chan et al., Stem cell functions are strongly influenced by mechanical cues in the environment. Thus far, biomaterials have served a crucial role in tissue engineering by offering control of the environment and its mechanics to direct stem cell functions. The future of biomaterial strategies for improving applications of iPSCs in tissue engineering remains optimistic with a high ceiling for advancement. Due to recent advances in fields such as biomaterial design and stereolithography, research efforts are establishing biomaterial systems that allow extremely precise spatiotemporal control and presentation of mechanical cues. These systems can be designed to facilitate iPSC reprogramming, self-renewal, differentiation, and morphogenesis in a stepwise manner. Ideally, such platforms will be developed to function with minimal inputs from the end user once the primary cell-material interactions are initiated. Therefore, future challenges will involve mediating developmental transitions in a growingly diverse population of cells. Advances in material science and engineering combined with an understanding of developmental biophysics and epigenetic regulation will be critical to facilitate this modern engineering effort.SL, DD, and J-WS wrote and contributed to the work.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The Scottish Intercollegiate Guidelines Network (SIGN) guideline on delirium is a major advance on existing guidelines on this condition. This is particularly important given the evidence it is frequently under-diagnosed and inadequately managed despite being common and frequently associated with significant patient and carer distress and poor outcomes. The guidelines recommend using the 4A’s test to help detect delirium. A bundle of mostly non-pharmacological therapies minimise the risk of developing delirium and can help those who develop the condition. The importance of medical optimisation by an experienced professional in those at risk of delirium is highlighted with new recommendations for people in intensive care and surgical settings. There is guidance on follow-up of people with delirium, which should become routine. This commentary piece focusses on areas with the greatest potential to improve the experience and outcomes of those with delirium, and briefly discusses areas of ongoing uncertainty.  Delirium is a state of acute deterioration in mental functioning that occurs soon after acute medical illness, surgery, trauma, or a change in medications . It causThe 4AT tool should be used for identifying patients with probable delirium in emergency department and acute hospital settings.Use of the 4AT tool could be considered for use in community or other settings for identifying patients with probable delirium.For intensive care unit settings, Confusion Assessment Method – Intensive Care Unit (CAM-ICU) or Intensive Care Delirium Screening Checklist (ICDSC) should be considered to help identify patients with probable delirium.○new focal neurological signs;○a reduced level of consciousness (not adequately explained by another cause);○a history of recent falls;○a head injury (patients of any age);○anticoagulation therapy.Computed tomography (CT) brain scan should not be used routinely but should be considered in hospital patients with delirium in the presence of:Electroencephalogram should be considered when there is a suspicion of epileptic activity or non-convulsive status epilepticus as a cause of a patient’s delirium.○orientation and ensuring patients have their glasses and hearing aids;○promoting sleep hygiene;○early mobilization;○pain control;○prevention, early identification, and treatment of postoperative complications;○maintaining optimal hydration and nutrition;○regulation of bladder and bowel function;○provision of supplementary oxygen, if appropriate.The following components should be considered as part of a package of care for patients at risk of developing delirium:With the aim of avoiding excessively deep anesthesia, depth of anesthesia should be monitored in all patients aged over 60 years who are under general anesthesia during surgery expected to last for more than one hour.The use of earplugs should be considered as part of a sleep-promotion strategy in intensive care.All patients at risk of delirium should have a medication review conducted by an experienced healthcare professional.○First, consider acute, life-threatening causes of delirium, including low oxygen level, low blood pressure, low glucose level, and drug intoxication or withdrawal.○Systematically identify and treat potential causes , noting that multiple causes are common.○Optimize physiology, management of concurrent conditions, environment (reduce noise), medications, and natural sleep, to promote brain recovery.○Specifically detect, assess causes of, and treat agitation and/or distress, using nonpharmacological means only if possible .Consideration should be given to imaging patients with non-resolving delirium where no clear cause is identified or there are features to suggest primary central nervous system pathology.Lumbar puncture should not be performed routinely on patients with delirium.Ward moves should be avoided wherever possible for patients at risk of delirium.Prior to surgery, patients and caregivers should be advised of the risk of developing delirium, to alleviate distress and help with management if it does occur.Where possible, assistance should be sought from a patient’s relatives and caregivers to deliver care to reduce the risk of delirium developing.Areas with patients at high-risk of delirium, such as trauma orthopaedic wards, should have protocols for commonly required medication  that contain choices for first-line treatments that minimise the risk of causing delirium.Promote cognitive engagement, mobilization, and other rehabilitation strategies.Patient records should be coded to highlight a previous episode of delirium so that hospital staff are aware of the increased risk on readmission.Ensure that delirium is noted in the discharge letter for the primary care team.All patients who have had delirium should be reviewed by the primary care team.In patients who have experienced delirium in intensive care units (ICU), consideration should be given to follow-up for psychological sequelae including cognitive impairment.Good Practice Points:The SIGN guideline specifically recommends routinely using the 4AT tool  in emergAlthough this is helpful and should minimize under-diagnosis of delirium, some questions remain. Should all admissions to hospital be screened or should detection tools be targeted, for example to those aged over 65? How should we monitor for incident delirium? In what patient groups and how frequently? Do available tools have utility in community and long-term care settings? There is currently insufficient evidence to answer these questions.Reinforced throughout the SIGN guideline is the importance of making the diagnosis of delirium and communicating it to the patient, their relatives, and all relevant healthcare professionals involved in their care. This includes clearly documenting the diagnosis in the clinical record and any transfer and discharge documentation. There are helpful sections on communication and patient education, with resources and checklists of important points to consider helping patients and relatives become better informed and more actively involved in their condition and treatment. This is especially pertinent where individuals lose their capacity to consent to investigation and treatment due to delirium.Moreover, the SIGN guidelines acknowledge that the evidence-base for effective treatments in delirium is weak. Nevertheless, it recommends following an established, multi-component pathway of interventions to help identify underlying causes, since delirium is usually multi-factorial. Though no specific pathway is recommended, a couple of potentially suitable examples (the TIME bundle and the Scottish Delirium Association pathway ) are incThe guidelines do not recommend routine use of any pharmacological treatment of delirium and particularly highlight a recent Cochrane review finding that antipsychotics did not reduce delirium severity, duration, or alter mortality  in non-IFinally, the guidelines make clear recommendations that all patients with delirium should be followed up in primary care to check for common sequela, including depression, post-traumatic stress disorder and enduring cognitive impairment that may lead to a diagnosis of dementia.Since treatments for delirium are of limited proven efficacy, many of the guideline recommendations focus on preventing delirium in those at high-risk of developing it. A multi-component non-pharmacological intervention promoting early mobilization, regular orientation, minimizing sensory impairment, and optimizing hydration and nutrition significantly reduces incident delirium rates by almost a third, but only in those at higher risk ,13. The Although the new guideline is broadly similar to the National Institute of Healthcare and Care Excellence  guidelines on delirium, there are some important differences. SIGN is more specific about how best to detect delirium, particularly in non-ICU settings. The recommendations for preventing delirium are essentially the same, though the updated SIGN guideline includes a stronger evidence base for multi-component interventions from recent studies. The new guidelines on investigating and treating delirium are much more detailed than that in NICE, with a stronger emphasis on non-pharmacological interventions and encouraging the active participation of family and caregivers. Conversely, the recommendations around pharmacological treatment are more reserved, with no specific recommendation for haloperidol or any other agent due to the lack of evidence of efficacy. Recommendations on follow-up are also included only in the SIGN guidelines.Guidelines have potential to do harm , especia


Microglia were previously regarded as a homogenous myeloid cell lineage in the mammalian central nervous system (CNS). However, accumulating evidences show that microglia in the brain and SC are quite different in development, cellular phenotypes and biological functions. Although this is a very interesting phenomenon, the underlying mechanisms and its significance for neurological diseases in association with behavioral and cognitive changes are still unclear. How microglia differ between these two regions and whether such diversity may contribute to CNS development and functions as well as neurological diseases will be discussed in this Perspective. Microglia are important for building and defending the central nervous system (CNS)  are consUsing various immunohistochemical markers for microglia, including tomato lectin, CD45, CD68, major histocompatibility complex (MHC)-II, CD11b/c (OX-42), and ionized calcium-binding adaptor molecule-1 (Iba1), researchers have observed generally similar developmental aspects of microglia in their entrance and colonization in the brain and the SC, but minor differences exist . In a stImportantly, much of the knowledge on developmental features of microglia in the SC comes from previous studies on human post-mortem samples . In the Other microglial phenotypes may also differ from those in the brain at adulthood . Fundameex vivo and after transition to an in vitro environment, and studied transcriptomes of various mouse CNS regions. However, neither study specifically addressed regional aspects of spinal microglial transcriptomic features in development and adulthood. In the study of Gosselin et al., gene family members of Fos and Kif seemed to be more enriched in the SC than the brain as a function of development in mice (–SPARC–IBA1+ microglia) was more prevalent in the juvenile SC and cerebellum as well as in the adult cerebellum and corpus callosum compared to the cortex and hippocampus (Using genome-wide chromatin gene expression profiling and/or single-cell RNA sequencing (scRNAseq) analysis, three recent pivotal studies included microglia in the SC . Matcovi in mice . Very repocampus . These sIt is still unclear what underlies the fundamental difference between spinal and brain microglia and whether this is mainly caused by intrinsic or extrinsic factors . Guiding+ and MHCII+ subpopulations as well as in expression of microglial activation genes underlined their differential responses to SNI as well as to the analgesic effect of minocycline, so that genes involved in M2 polarization and phagocytosis were upregulated in the spinal dorsal horn after SNI compared to Sham, but were downregulated in the cortex , spinal cord injury (SCI), as well as spinal nerve injury models (Details on the respective models are provided in these recent reviews ; Table 1e cortex . Howevere cortex , implyinin vitro cell culture study on morphological properties and secretion of inflammatory and trophic effectors by microglia derived from the brain or spinal cord of neonatal rats, Studies on TBI and SCI have reported that acute inflammatory response to traumatic injury is significantly greater in the SC than in the cerebral cortex. A more careful dissection on these differences was thoroughly provided in an earlier review  and therBrain microglia may be more important for regulating neuron-mediated cognition and emotion, whereas spinal microglia more relevant for controlling neuron-mediated sensory-motor functions. This may commit brain and spinal microglia to different CNS conditions, e.g., psychiatric and mental disorders versus sensory-motor neurological diseases. It is doubtless that more and more researchers are recognizing the existence of microglial regional specificity, but more vigorous investigations are still required for us to more deeply understand this interesting phenomenon.All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


During the last few years, the understanding of the dysregulated hydrogen ion dynamics and reversed proton gradient of cancer cells has resulted in a new and integral pH-centric paradigm in oncology, a model which embraces most if not all aspects of cancer, from etiopathogenesis to treatment. The cancer-selective abnormality of intracellular alkalinization along with extracellular acidification  of all types of solid tumors and leukemic cells is finally recognized as a specific and most selective hallmark of malignancy. As a consequence of this acid-base homeostatic failure of cellular hydrogen ion (H+) dynamics, the attempt to induce cellular acidification using proton transport and pump inhibitors (PTIs) and other intracellular acidifiers of different origins is becoming a new therapeutic concept and selective target of cancer treatment. A full issue containing fourteen reviews on the different aspects of the new pH-centric anticancer paradigm has been recently published .in situ to invasive breast cancer, with the precancerous lesion already showing a higher than normal proton export rate [Recently, it has been demonstrated that H+ efflux alone is sufficient to induce dysplasia and potentiate growth and invasion by oncogenic Ras and, furthermore, that inhibiting this H+ efflux produced cell death in invasive primary tumor cell lines through intracellular acidification and NHE inhibition. Similar results have been obtained by Fliegel`s group, showing that NHE-mediated H+ extrusion by itself has a carcinogenic effect on breast cells. In these studies, NHE1 hyperactivity appears to be an early and decisive driver in breast cancer carcinogenesis. Furthermore, an elevated pHi and increased proton efflux with a secondary acidified microenvironment (PCR) has been implicated in the transition and progression from precancerous ductal carcinoma ort rate . We haveort rate , 3.Recently, we integrated a large myriad of known carcinogenic factors from many different natures known to be the main cause of cancer via a universal mediating mechanism: an increase of cell pH and/or stimulation of NHE activity (see Table 2 in reference) . HoweverBeyond cariporide , among tCancer research has always been about finding the weak points of malignant tumors in order to target them selectively and avoid damaging normal cells. This “magic bullet-like” approach to treatment demands knowing the main selective characteristics of cancer cells and tissues that are not present in normal cells. The pH dynamics of cancer cells, and specifically, the pH- or proton gradient reversal, now appears as a specific abnormality and even the most differential metabolic trait of all and each type of cancer. Therefore, this new cancer paradigm should and hopefully soon would be successfully exploited in any approach to selective cancer therapeutics , 7.


Birds exhibit two types of sleep that are in many respects similar to mammalian rapid eye movement (REM) and non-REM (NREM) sleep. As in mammals, several aspects of avian sleep can occur in a local manner within the brain. Electrophysiological evidence of NREM sleep occurring more deeply in one hemisphere, or only in one hemisphere – the latter being a phenomenon most pronounced in dolphins – was actually first described in birds. Such asymmetric or unihemispheric NREM sleep occurs with one eye open, enabling birds to visually monitor their environment for predators. Frigatebirds primarily engage in this form of sleep in flight, perhaps to avoid collisions with other birds. In addition to interhemispheric differences in NREM sleep intensity, the intensity of NREM sleep is homeostatically regulated in a local, use-depended manner within each hemisphere. Furthermore, the intensity and temporo-spatial distribution of NREM sleep-related slow waves varies across layers of the avian hyperpallium – a primary visual area – with the slow waves occurring first in, and propagating through and outward from, thalamic input layers. Slow waves also have the greatest amplitude in these layers. Although most research has focused on NREM sleep, there are also local aspects to avian REM sleep. REM sleep-related reductions in skeletal muscle tone appear largely restricted to muscles involved in maintaining head posture. Other local aspects of sleep manifest as a mixture of features of NREM and REM sleep occurring simultaneously in different parts of the neuroaxis. Like monotreme mammals, ostriches often exhibit brainstem-mediated features of REM sleep (muscle atonia and REMs) while the hyperpallium shows EEG slow waves typical of NREM sleep. Finally, although mice show slow waves in thalamic input layers of primary sensory cortices during REM sleep, this is not the case in the hyperpallium of pigeons, suggesting that this phenomenon is not a universal feature of REM sleep. Collectively, the local aspects of sleep described in birds and mammals reveal that wakefulness, NREM sleep, and REM sleep are not always discrete states. Wakefulness and sleep, as well as the two types of sleep found in mammals and birds – rapid eye movement (REM) and non-REM (NREM) sleep – are often treated as mutually exclusive states distinguished by a suite of behavioral, electroencephalographic, and electromyographic traits. In mammals and birds, NREM sleep is distinguished from wakefulness by the presence of high-amplitude slow waves in electroencephalogram (EEG) or local field potential (LFP) recordings. These slow waves result from the slow oscillation, or alternation, of neuronal membrane potentials between a hyperpolarized state with neuronal quiescence and a depolarized state with action potentials . Slow walocal sleep is not restricted to these animals. As new technology allows more brain regions to be examined simultaneously, it is becoming evident that even in conventional mammalian models for studying sleep, such as rodents and humans, traits used to define different states can occur at the same time in different brain regions , or more deeply with one hemisphere than the other (asymmetric sleep) undoubtedly contributed to current views regarding the local nature of NREM sleep . These aLarus glaucescens). Later, Anas platyrhynchos), with SWA levels in the hemisphere contralateral to the open eye higher than (bihemispheric) wakefulness, but lower than that occurring during bihemispheric NREM sleep (Fregata minor) in the wild where eye state could not be monitored  varies across studies depending, in part, on the relative emphasis given to EEG or behavioral indicators of sleep. In studies that emphasize EEG activity, the boundaries between these states are defined by the degree of interhemispheric asymmetry in NREM sleep-related EEG SWA. In marine mammals, an asymmetry index (AI) in the level of SWA has been calculated as: AI = (left hemisphere SWA – right SWA)/(left SWA + right SWA) ,b. The cEM sleep . Given tEM sleep . Finallyonitored .None of the terms used to describe the asymmetric forms of sleep and wakefulness fully capture all EEG and behavioral aspects of these states. Unihemispheric sleep inherently implies unihemispheric wakefulness. This seemingly makes sense when applied to fur seals, but not dolphins. When sleeping in the water, fur seals float on one side with their nostrils held above the surface to breath. This posture is maintained by unilateral movements of the front flipper in the water which is presumably controlled by the contralateral awake hemisphere. Interestingly, the eye facing down in the water is open while the other is closed , possiblDetermining whether sleeping with one eye open serves an adaptive function would likely enhance our understanding of asymmetric/unihemispheric sleep and the relative importance that should be given to eye state. One possibility is that having one eye open is simply a functionless byproduct of having one hemisphere awake (or sleeping less deeply) for some other reason. For example, when sleeping in the water, do fur seals keep one eye open to watch for threats, or is this simply an epiphenomenon of keeping one hemisphere awake to control unilateral flipper movements? Although it is unknown whether the open eye can detect threats in fur seals, research on birds and dolphins provide insight into the answer. Captive ducks sleeping on the ground at the edge of a group spend a greater proportion of NREM sleep with one eye open (rather than both eyes closed) when compared to ducks sleeping flanked on both sides by other ducks ,b. MoreoLagenorhynchus obliquidens) swimming side-by-side in a group of four directed their open eye toward the other dolphins  fly from Alaska to New Zealand in 8.1 days spanning 11,690 km of sustained flight (Tachymarptis melba) and Common swifts (Apus apus) can fly for 200 and 300 days, respectively  and juvenile saltwater crocodiles (Crocodylus porosus) direct the open eye toward potential threats  project to GABA/glycine inhibitory neurons in the ventromedial medulla (vmM) which in turn hyperpolarize somatic motor neurons resulting in atonia of the postural muscles . The locThe local regulation of muscle tone during avian REM sleep may have implications for understanding the functions of atonia. Traditionally, atonia is thought to prevent dream-related motor cortex output from causing animals to act out their dreams during REM sleep . AccordiRather than being a functionless byproduct of dreaming, twitches appear to play an important role in the development and perhaps maintenance of the motor cortex. During wakefulness, deliberate movements are accompanied by a corollary discharge that effectively informs the sensorimotor cortex that a movement has been initiated and the resulting sensory feedback (reafference) should be ignored. By contrast, during REM sleep, reafference resulting from twitches generated by the brainstem reaches the sensorimotor cortex where it induces large responses that are thought to play an active role in the development of sensorimotor neural circuits . Atonia Becoming completely atonic and twitchy does not seem to pose a problem for very young birds which do not need to maintain up-right postures. However, it is unclear how adult birds reconcile this seemingly fundamental need for atonia and twitching with the local regulation of muscle tone during REM sleep. Presumably, the muscle groups controlling the digits, legs, etc., involved in standing also need to twitch. Indeed, the fact that reduced muscle tone persists, in at least some muscle groups, during certain postures emphasizes its importance; if it did not serve an important function natural selection should have done away with it altogether. Given the presence of atonia in the neck muscles of geese during REM sleep only when the head is supported , it is cTachyglossus aculeatus; Ornithorhynchus anatinus; The heterogeneity of states is not limited to the combination of wakefulness and NREM sleep, in the case of unihemispheric sleep, or in topographic variations in SWA during NREM sleep or REM sleep-related muscle tone. Comparative research reveals that mixed states can arise through the combination of aspects of NREM sleep occurring simultaneously with features of REM sleep. The first evidence for such mixture was gleaned from several studies on monotreme mammals. Owing to the retention of ancestral traits, such as egg-laying , monotreStruthio camelus) also have a monotreme-like sleep state characterized by EEG slow waves occurring in conjunction with REMs under closed eyelids, and behavioral and physiological signs of reduced neck muscle tone (Eudromia elegans) has sleep states like those of all other birds studied, as characterized by the EEG, EOG, EMG and head movements captured by video recordings and accelerometry   does not exhibit wake-like cortical activation during putative REM sleep, but rather a novel beta rhythm, rarely observed during wakefulness  . Perhapsefulness . CollectRecent studies suggest that slow waves occurring during REM sleep might be more common than previously thought, even in more traditional mammalian models for investigating sleep, such as rodents and humans. Intracortical records from mice revealed that slow waves persist in the thalamic input layers of primary sensory cortices during REM sleep . By contRecent intra-“cortical” recordings from pigeons demonstrate that slow waves occurring in the thalamic input layers of primary sensory areas is not a universal feature of animals with REM sleep. As described above, during NREM sleep, slow waves in the visual hyperpallium – the avian homolog of the mammalian primary visual cortex – are most evident in thalamic input layers. However, during REM sleep, these layers show a large reduction in slow wave power from prior NREM sleep levels . FurtherIn several respects, the local aspects of sleep are similar in mammals and birds. Nonetheless, there are also some potentially informative differences. In both groups, at least some species mitigate the simultaneous ecological need to be awake and the physiological need to sleep by engaging in NREM sleep unihemispherically. Although the evidence for sleep homeostasis at the hemispheric level remains somewhat equivocal, the presence of local use-dependent homeostasis within a hemisphere suggests that homeostasis at the hemispheric level is likely. In both mammals and birds, thalamic input layers play a prominent role in the initiation of propagating slow waves. Despite the similar local aspects of NREM sleep, birds exhibit local aspects of REM sleep not found in mammals. Although birds show visible signs of reduced tone in the muscles supporting the head during REM sleep, the muscles involved in maintaining a standing posture (even on one foot) rarely show reductions in tone during REM sleep. In addition, birds are able to modulate tone in the neck muscles to accommodate different head positions during REM sleep. Like monotreme mammals, ostriches exhibit a mixed sleep state characterized by cortical slow waves occurring with brainstem-mediated features of REM sleep. However, comparisons with other birds and reptiles, challenge the hypothesis that this mixed sleep state reflects an ancestral form of sleep in mammals or birds. Instead, an emerging pattern from recent comparative work is that the EEG correlates of REM sleep may be far more diverse than previously recognized: ostriches have slow waves, but their close tinamou relatives have activation typical of other birds; pogona lizards have wake-like cortical activation, but tegu lizards exhibit a beta rhythm rarely found during wakefulness; and mice exhibit slow waves in the thalamic input layers of the primary visual cortex, but pigeons show activation in these layers. We suspect that as the diversity of species and the number of brain regions examined increases, our ability to package sleep into two, simple, mutually exclusive states will become even more difficult. Although this may tax our ability to define sleep, this diversity also serves as a rich resource for exploring the mechanisms and functions of sleep-related brain activity.NR and JL wrote the initial draft of the manuscript. All authors provided the input on the manuscript. JL and JvdM contributed to the figures.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The potential effects of dishware size on energy intake are unclear, as many previous studies have been of low methodological quality. A newly published paper by Kosīte et al.  reports findings from a rigorous, pre-registered investigation of the effects of manipulating plate size on total energy intake within a single eating occasion. This Editorial considers the implications of these new findings in light of previous evidence pertaining to the efficacy of behavioral nudges in particular, and in relation to contextual drivers of food consumption more generally. We conclude that the potential impact of behavioral nudges may have been exaggerated in the past, and call for future high-quality randomized controlled trials to establish whether reducing dishware size and other behavioral nudges might offer an effective complement to more comprehensive, multi-level interventions to reduce overconsumption of foods and beverages at a population-level. Despite decades of focussed study and intervention, overconsumption of foods and beverages remains a pervasive problem internationally –3. SelecThe idea that using smaller sized dishware might nudge individuals to eat less is consistent with individual-level behavioral approaches. The rationale for using smaller plates is informed by the Delboeuf visual illusion, whereby humans perceive a circle surrounded by a small circle as larger compared to when it is surrounded by a larger circle . AppliedThe newly published paper by Kosīte et al.  reports This lack of clarity in findings from systematic reviews is compounded by the fact that all three reviews included multiple studies authored by the Cornell Food and Brand Lab, which has come under scrutiny for possible scientific misconduct and recently retracted or corrected numerous publications. Although none of the retracted articles were included in the reviews, there is concern that these studies often reported large effect sizes when others found no evidence of effects , 17. NotGiven this context, the current well-conducted RCT by Kosīte et al.  is very So how important are these results? Although the study was rigorous and the overall differences between groups was very small and not statistically significant, the authors duly caution that the observed wide confidence intervals do not rule out the possibility of a small to medium effect in either direction. Thus, further high quality studies are needed, as no single study can ever offer a definitive answer to any scientific question due to unavoidable biases that exist even within well-designed RCTs . This stScientists will undoubtedly disagree as to the probative value of further studies in this area, and the nature of any studies that might be undertaken, based on whether they subscribe to an individual-level behavioral or a multi-level socioecological paradigm of health. For instance, some might consider it worthwhile to examine whether manipulating dishware size might influence food consumption under certain conditions; such as if individuals eat when distracted, when using certain types of dishware (e.g. bowls vs plates), when food is self-served rather than pre-portioned, if meals entail amorphous mixed dishes or discrete snack items, among distinct population subgroups or at certain times of day. Clearly an endless array of investigations could be pursued at considerable time, effort and expense. However, we would suggest that key questions might instead relate to whether manipulating dishware size has value at a broader population-level; such as one aspect of broader multi-level policy interventions within publicly funded institutions . Such studies would entail long-term investigations of the effects of manipulating dishware size on intake of commonly consumed foods across multiple meals and days, and would need to be performed in large, diverse, population-based samples under free-living conditions.In our estimation, the notion that one or even a series of small behavioral nudges could resolve problems related to overconsumption of foods and beverages belies a relatively simplistic understanding of the primary drivers of food consumption. The science of nudging intimates that individuals make ‘poor dietary choices,’ and that these poor choices can be relatively easily corrected, downplaying the broader contextual issues at play. A more comprehensive perspective regards food consumption as a complex practice shaped by the opportunities and constraints imposed by individuals’ broader social, cultural, economic and political contexts. For example, individuals who are working multiple low-wage jobs may use food to cope with the stress generated by difficult employment conditions, while individuals who are experiencing food insecurity may eat more when food is abundant given future expectations of scarcity . People Governments have many policy levers available to them, however their resources are not infinite. An excessive focus on individual-level behavioral interventions risks crowding out more powerful interventions that address the fundamental causes of overeating rooted in social, cultural, economic and political contexts. Although behavioral nudges need not be abandoned and can be integrated within more comprehensive multi-level socioecological approaches, their potential impact may have been exaggerated in the past. Given the poor quality of much of the prior research pertaining to the effects of manipulating dishware size and the null findings reported herein, there is at present no substantive evidence upon which to recommend the use of smaller dishware to curb overconsumption. Fortunately, as evidenced by Kosīte et al’s work, it is possible to conduct high-quality studies in this respect. We would therefore encourage future high-quality RCTs to establish whether reducing dishware size and other behavioral nudges might offer an effective complement to more comprehensive multi-level interventions to reduce overconsumption of foods and beverages at a population-level.