Differences in spatial versus temporal reaction norms for spring and autumn phenological events

For species to stay temporally tuned to their environment, they use cues such as the accumulation of degree-days. The relationships between the timing of a phenological event in a population and its environmental cue can be described by a population-level reaction norm. Variation in reaction norms along environmental gradients may either intensify the environmental effects on timing (cogradient variation) or attenuate the effects (countergradient variation). To resolve spatial and seasonal variation in species’ response, we use a unique dataset of 91 taxa and 178 phenological events observed across a network of 472 monitoring sites, spread across the nations of the former Soviet Union. We show that compared to local rates of advancement of phenological events with the advancement of temperature-related cues (i.e., variation within site over years), spatial variation in reaction norms tend to accentuate responses in spring (cogradient variation) and attenuate them in autumn (countergradient variation). As a result, among-population variation in the timing of events is greater in spring and less in autumn than if all populations followed the same reaction norm regardless of location. Despite such signs of local adaptation, overall phenotypic plasticity was not sufficient for phenological events to keep exact pace with their cues—the earlier the year, the more did the timing of the phenological event lag behind the timing of the cue. Overall, these patterns suggest that differences in the spatial versus temporal reaction norms will affect species’ response to climate change in opposite ways in spring and autumn.

To stay tuned to their environment, species need to respond to both short- and long-term variation in climatic conditions. In temperate regions, favorable abiotic conditions, key resources, and major enemies may all occur early in a warm year, whereas they may occur late in a cold year. Coinciding with such factors may thus come with pronounced effects on individual fitness and population-level performance (1–4). As phenological traits also show substantial variability within and among populations, they can be subject to selection in nature (5–7), potentially resulting in patterns of local adaptation (8–10).At present, the rapid rate of global change is causing shifts in species phenology across the globe (11–13). Of acute interest is the extent to which different events are shifting in unison or not, sometimes creating seasonal mismatches and functionally disruptive asynchrony (3, 14–16). If much of the temporal and spatial variation in seasonal timing is a product of phenotypic plasticity, then changes can be instant, and sustained synchrony among interaction partners will depend on the extent to which different species react similarly to short-term variation in climatic conditions. If geographic variation in phenology reflects local adaptive evolutionary differentiation, then, in the short term, as climate changes, phenological interactions may be disrupted due to the lag as adaptation tries to catch up (17–19). By assuming that space can substitute time, it is possible to make inference about the role that adaptation to climate may play. How well species stay in synchrony will then depend on the extent to which local selective forces act similarly or differently on different species and events.Local adaptation in phenology may take two forms. 1) The magnitude of phenological change might vary along environmental gradients in ways that intensify the environmental effects on phenological traits, a process known as cogradient variation (Fig. 1B). In such a case, the covariance between the genetic influences on phenological traits and the environmental influences is positive. Under this scenario, the effect of environmental variation over space and time will be larger than if all populations were to follow the same reaction norm regardless of location. 2) Genotypes might counteract environmental effects, thereby diminishing the change in mean trait expression across the environmental gradient. In such a case, the effect of environmental variation over space and time will be smaller than if all populations were to follow the same reaction norm regardless of location. This latter scenario, termed countergradient variation, occurs when genetic and environmental influences on phenotypic traits oppose one another (Fig. 1C) (20, 21).Open in a separate windowFig. 1.Schematic illustration showing slopes of phenology on temperature. Adapted with permission from ref. 30. A corresponds to phenological plasticity with respect to temperature and no local adaptation. B reveals phenological plasticity with respect to temperature plus cogradient local adaptation. C reveals phenological plasticity with respect to temperature plus countergradient local adaptation. For each scenario, we have included two examples of events showing this type of pattern in our data. For the exact climatic cues related to these biotic events, see SI Appendix, Table S1. In each plot, the red lines correspond to the within-population reaction norms through time (i.e., temporal slopes within locations), and the blue line corresponds to the between-population reaction norm (i.e., spatial slopes). If all populations respond alike, then the same reaction norm will apply across all locations, and individuals will respond in the same way to the cue no matter where they were, and no matter whether we examine responses within or between locations. If this was the case, then the reaction norm would be the same within (red lines) and between locations, and the blue and the red slopes would be parallel (i.e., their slopes identical). This scenario is depicted in A. What we use as our estimate of local adaptation is the difference between the two, i.e., whether the slope of reaction norms within populations differs from that across populations. If the temporal slopes are estimated at a relatively short time scale (as compared to the generation length of the focal organisms), then we can assume that within-location variation in the timing of the event reflects phenotypic responses alone, not evolutionary change over time. This component is then, per definition, due to phenotypic plasticity as such, i.e., to how individuals of a constant genetic makeup respond to annual variation in their environment. By comparison, the spatial slope (i.e., the blue line) is a sum of two parts: first, it reflects the mean of how individuals of a constant genetic makeup respond to annual variation in their environment, i.e., the temporal reaction norm defined above. These means are shown by the red dots in A–C. However, second, if populations differentiate across sites, then we will see variation in their response to long-term conditions, with an added element in the spatial slope reflecting mean plasticity plus local adaptation. Therefore, if the spatial slope differs from the temporal slope, this reveals local adaptation (see Materials and Methods for further details). Such local adaptation in phenological response may take two forms. 1) The magnitude of phenological change might vary along environmental gradients in ways that intensify the environmental effects on phenological traits, a process known as cogradient variation (Fig. 1B). In such a case, the covariance between the genetic influences on phenological traits and the environmental influences is positive. Under this scenario, variation in the environmental cue over space and time will cause larger variation in phenological timing than if all populations were to follow the same reaction norm regardless of location. 2) Genotypes might counteract environmental effects, thereby diminishing the change in mean trait expression across the environmental gradient. In such a case, the effect of variation in the environmental cue over space and time will be smaller than if all populations were to follow the same reaction norm regardless of location. This latter scenario, termed countergradient variation, occurs when genetic and environmental influences on phenotypic traits oppose one another (C).For phenology, the overall prevalence of co- versus countergradient patterns is crucial, as it will dictate the extent to which local adaptation will either accentuate or attenuate phenological responses to temporal shifts in climate (10). Across environmental gradients in space, the relative prevalence of counter- versus cogradient variation in spring versus autumn will critically modify how climatic variation affects the length of the activity period of the entire ecological community. Overall, geographic variation in the activity period will be maximized when events in autumn and spring differ in terms of whether they adhere to patterns of co- or countergradient variation.Although the study of individual species and local species communities has revealed fine-tuning of species to local conditions (22), and a wealth of studies report shifts in phenology worldwide (23), we still lack a general understanding of how the two tie together: how strong is local adaptation in the timing of events, and how do they vary across the season? Here, a major hurdle to progress has been a skew in the focus of past studies: our current understanding of climatic effects on phenology has been colored by springtime events (24–26), whereas events with a mean occurrence later in the season have been disproportionately neglected (27). To achieve satisfactory insight into how climate and its change affect the timing of biological activity across the season, we should thus ask how strongly phenology is influenced by climatic variation, what part of this response reflects phenotypic plasticity and what part evolutionary differentiation, and how the relative imprint of the two varies across the season. Addressing these pertinent questions is logistically challenging (e.g., ref. 28). Therefore, few studies have tackled them outside of the laboratory (29).Phillimore and coworkers (10, 30) proposed an elegant technique for identifying the relative roles of plasticity and local adaptation in generating spatiotemporal patterns of phenological variation. The rationale is to use a space versus time comparison (10, 30) (but see ref. 31 for criticism), drawing on the realization that at any one site, local conditions will vary between years. To be active at the right time, species will thus need to respond to temporal variation in climatic conditions. Let us assume that a focal species times some aspect of its annual activity (a species-specific “phenological event”) by reacting to a single environmental cue (e.g., the crossing of a given temperature sum). Now, if there were no differentiation between populations and all populations followed the same reaction norm, then with variation in the relative timing of the cue over time, all populations would react in the same way to the same cue regardless of spatial location (Fig. 1A). At the level of population means across space (blue line in Fig. 1A), we would then see a relationship between phenological event and cue timing identical to year-to-year variation within locations (red lines in Fig. 1A). However, if populations differentiate across sites, then we will see an added component in the spatial slope, reflecting the contribution of local adaptation to the mean phenology of the populations. By subtracting the within-population temporal slope from the spatial slope, we will thus achieve a direct measure of local adaptation (10), henceforth called Δb (30).Importantly, the temporal slope (i.e., the local phenological response to local year-to-year variation in the cue) can be either steeper or more shallow than the spatial slope (Fig. 1B vs. Fig. 1C)—the former being a sign of countergradient local adaptation, the latter of cogradient local adaptation (20, 21, 32). For a worked-through example of how this methodology is applied to the current data, see SI Appendix, Text S1.Here, we adopt temperature sums as widely used predictors of phenological events (33–35) and treat the difference between the spatial and temporal slopes of phenological events on such sums as our estimates of local adaptation in reaction norms (SI Appendix, Text S1). Pinpointing the relative roles of plasticity and microevolution from spatiotemporal observations in the absence of direct measures of fitness will, per necessity, rely on several assumptions (for a full discussion, see ref. 36). However, given the adequate precaution, such quantification allows a tractable way toward estimating local adaption on a large scale (8, 10, 30, 36–38).A key requirement for the successful application of this approach to resolving patterns across events of different relative timing is the existence of abundant data covering a large geographic area (30, 36). The extensive phenological data-collection scheme implemented at hundreds of nature reserves and other monitoring sites within the area of the former Soviet Union offers unique opportunities for addressing community-level phenology across a large space and long time (39). From this comprehensive dataset spanning 472 monitoring sites, 510,165 events and a time series of up to 118 y (Fig. 2 and ref. 39), we selected those 178 phenological events for which we have at least 100 data points that represent at least 10 locations (SI Appendix, Table S1). These events concerned 91 distinct taxa (SI Appendix, Table S1).Open in a separate windowFig. 2.Study sites and spatiotemporal patterns in climatic and phenological data. A shows the depth of the data and the spatial distribution of monitoring sites, with the size of the symbol proportional to the number of events scored locally. Since the selection of sites differed between events (39), in A, we have pooled sites located within 300 km from each other for illustration purposes. B shows the mean timing (day of year) of a phenological event: the onset of blooming in dandelion (Taraxacum officinale). C shows the mean timing (day of year) of a climatic event: the day of the year when the temperature sum providing the highest temporal slope for the onset of blooming in dandelion was first exceeded, computed as the mean over the years considered in B. For a worked-through example estimating reaction norms and metrics of local adaptation (Δb) for this species, see SI Appendix, Text S1.To express data on species phenology and abiotic conditions in the same currency, we related the dates of the phenological events (e.g., the first observation of an animal, or first flowering time of a plant species; SI Appendix, Fig. S1) to the dates when a given thermal sum (34, 35) was first exceeded. This choice of units has a convenient consequence in terms of the interpretation of slope values: if the date of phenology changes follows one-to-one the date of attaining a given temperature sum, then the slope will be one—an assumption frequently made but rarely tested in studies based on growth-degree days. The observed reaction norms can then be compared to this value. A value below 1 will signal undercompensation, i.e., that the earlier the cue, the larger the relative delay of the phenological event compared to its cue. By contrast, a value larger than 1 would signal overcompensation, i.e., that with an advancement of the cue, the timing of the phenological event will be advanced even more.Since thermal sums can be formed using a variety of thresholds, we used a generic approach and considered dates for exceeding a wide range of both heating and chilling degree-day sums (34, 35) (see Material and Methods for more information). As there is also evidence that sensitivity to temperature arises after a certain time point (13, 36), we calculated each heating and chilling degree-days sum for a range of starting dates. For each of the resulting 2,926 events, we then picked the variable that offered the highest temporal slope estimate, i.e., the largest within-location change in the timing of the event with a change in the timing of the cue (see Material and Methods for more information). Following the rationale outline above, this will be the most appropriate optimization criterion, since it selects the cue to which the phenological event responds the strongest to over time.     

Eulerian Algorithms for Computing the Forward Finite Time Lyapunov Exponent Without Finite Difference upon the Flow Map

In this paper, effective Eulerian algorithms are introduced for the computation of the forward finite time Lyapunov exponent (FTLE) of smooth flow fields. The advantages of the proposed algorithms mainly manifest in two aspects. First, previous Eulerian approaches for computing the FTLE field are improved so that the Jacobian of the flow map can be obtained by directly solving a corresponding system of partial differential equations, rather than by implementing certain finite difference upon the flow map, which can significantly improve the accuracy of the numerical solution especially near the FTLE ridges. Second, in the proposed algorithms, all computations are done on the fly, that is, all required partial differential equations are solved forward in time, which is practically more natural. The new algorithms still maintain the optimal computational complexity as well as the second order accuracy. Numerical examples demonstrate the effectiveness of the proposed algorithms.     

New local hyperthermia using dextran magnetite complex (DM) for oral cavity: experimental study in normal hamster tongue

The possibility of dextran magnetite complex (DM) as a new hyperthermic material was examined in this study. DM suspension of 56 mg ml(-1) iron concentration was locally injected into the normal tongue of golden hamster. DM injected tongues were heated by 500 kHz alternating current (AC) magnetic field and its serial changes in temperature were recorded at 30-s intervals. The temperature of DM injected tongue was maintained at about 43.0-45.0 degrees C for 30 min by changing the AC magnetic field intensity. While temperature elevations of the contralateral tongue and the rectum were only of minor degree. In experiment on the extent of heating area, there was correlation between volume of black stain area and amount of the injected DM suspension (Y = - 18.1 + 1.94X, r = 0.931, P < 0.0001, n = 9 ). Histological examination after heating revealed brown uniform DM accumulation in the connective tissue between fibers of the tongue muscle. Except for vascular dilatations, no tissue damage was seen in the heated tongue. Thus, DM which has the possibility of selective and uniform heating in local hyperthermia might be useful for oral cancer therapy.     

Effect of heating and enzymatic hydrolysis on casein cow milk sensitivity in Moroccan population

The objectives of the present work were first to evaluate the sensitivity to cow raw milk of the population of Fez, and then to study the effect of heating and pepsin hydrolysis on the allergenicity of casein. A cross-sectional study was carried out in Fez Hospitals, in which 1000 patients were recruited to establish a sera bank used to evaluate specific IgE to cow milk and to casein. Then, we evaluated the reaction of human IgE to heated and pepsin-hydrolysed casein. The results showed that 11.5% of the population studied self-reported reactions to foods. From them, 3.6% reported allergy to milk. Evaluation of specific IgE to cow raw milk showed that 11.9% of patients presented higher specific IgE levels. The treatments of casein indicated that both heating and pepsin hydrolysis totally decreased its binding on the human IgE.     

Topological sensitivity analysis for systems biology

Mathematical models of natural systems are abstractions of much more complicated processes. Developing informative and realistic models of such systems typically involves suitable statistical inference methods, domain expertise, and a modicum of luck. Except for cases where physical principles provide sufficient guidance, it will also be generally possible to come up with a large number of potential models that are compatible with a given natural system and any finite amount of data generated from experiments on that system. Here we develop a computational framework to systematically evaluate potentially vast sets of candidate differential equation models in light of experimental and prior knowledge about biological systems. This topological sensitivity analysis enables us to evaluate quantitatively the dependence of model inferences and predictions on the assumed model structures. Failure to consider the impact of structural uncertainty introduces biases into the analysis and potentially gives rise to misleading conclusions.Using simple models to study complex systems has become standard practice in different fields, including systems biology, ecology, and economics. Although we know and accept that such models do not fully capture the complexity of the underlying systems, they can nevertheless provide meaningful predictions and insights (1). A successful model is one that captures the key features of the system while omitting extraneous details that hinder interpretation and understanding. Constructing such a model is usually a nontrivial task involving stages of refinement and improvement.When dealing with models that are (necessarily and by design) gross oversimplifications of the reality they represent, it is important that we are aware of their limitations and do not seek to overinterpret them. This is particularly true when modeling complex systems for which there are only limited or incomplete observations. In such cases, we expect there to be numerous models that would be supported by the observed data, many (perhaps most) of which we may not yet have identified. The literature is awash with papers in which a single model is proposed and fitted to a dataset, and conclusions drawn without any consideration of (i) possible alternative models that might describe the observed behavior and known facts equally well (or even better); or (ii) whether the conclusions drawn from different models (still consistent with current observations) would agree with one another.We propose an approach to assess the impact of uncertainty in model structure on our conclusions. Our approach is distinct from—and complementary to—existing methods designed to address structural uncertainty, including model selection, model averaging, and ensemble modeling (2–9). Analogous to parametric sensitivity analysis (PSA), which assesses the sensitivity of a model’s behavior to changes in parameter values, we consider the sensitivity of a model’s output to changes in its inherent structural assumptions. PSA techniques can usually be classified as (i) local analyses, in which we identify a single “optimal” vector of parameter values, and then quantify the degree to which small perturbations to these values change our conclusions or predictions; or (ii) global analyses, where we consider an ensemble of parameter vectors (e.g., samples from the posterior distribution in the Bayesian formalism) and quantify the corresponding variability in the model’s output. Although several approaches fall within these categories (10–12), all implicitly condition on a particular model architecture. Here we present a method for performing sensitivity analyses for ordinary differential equation (ODE) models where the architecture of these models is not perfectly known, which is likely to be the case for all realistic complex systems. We do this by considering network representations of our models, and assessing the sensitivity of our inferences to the network topology. We refer to our approach as topological sensitivity analysis (TSA).Here we illustrate TSA in the context of parameter inference, but we could also apply our method to study other conclusions drawn from ODE models (e.g., model forecasts or steady-state analyses). When we use experimental data to infer parameters associated with a specific model it is critical to assess the uncertainty associated with our parameter estimates (13), particularly if we wish to relate model parameters to physical (e.g., reaction rate) constants in the real world. Too often this uncertainty is estimated only by considering the variation in a parameter estimate conditional on a particular model, while ignoring the component of uncertainty that stems from potential model misspecification. The latter can, in principle, be considered within model selection or averaging frameworks, where several distinct models are proposed and weighted according to their ability to fit the observed data (2–5). However, the models tend to be limited to a small, often diverse, group that act as exemplars for each competing hypothesis but ignore similar model structures that could represent the same hypotheses. Moreover, we know that model selection results can be sensitive to the particular experiments performed (14).We assume that an initial model, together with parameters or plausible parameter ranges, has been proposed to describe the dynamics of a given system. This model may have been constructed based on expert knowledge of the system, selected from previous studies, or (particularly in the case of large systems) proposed automatically using network inference algorithms (15–19), for example. Using TSA, we aim to identify how reliant any conclusions and inferences are on the particular set of structural assumptions made in this initial candidate model. We do this by identifying alterations to model topology that maintain consistency with the observed dynamics and test how these changes impact the conclusions we draw (Fig. 1). Analogous to PSA we may perform local or global analyses—by testing a small set of “close” models with minor structural changes, or performing large-scale searches of diverse model topologies, respectively. To do this we require efficient techniques for exploring the space of network topologies and, for each topology, inferring the parameters of the corresponding ODE models.Open in a separate windowFig. 1.Overview of TSA applied to parameter inference. (A) Model space includes our initial candidate model and a series of altered topologies that are consistent with our chosen rules (e.g., all two-edge, three-node networks, where nodes indicate species and directed edges show interactions). One topology may correspond to one or several ODE models depending on the parametric forms we choose to represent interactions. (B) We test each ODE model to see whether it can generate dynamics consistent with our candidate model and the available experimental data. For TSA, we select a group of these compatible models and compare the conclusions we would draw using each of them. (C) Associated with each model m is a parameter space Θ_m (gray); using Bayesian methods we can infer the joint posterior parameter distribution (dashed contours) for a particular model and dataset. (D) In some cases, equivalent parameters will be present in several selected models (e.g., θ₁, which is associated with the same interaction in models a–c). We can compare the marginal posterior distribution inferred using each model for a common parameter to test whether our inferences are robust to topological changes, or rely on one specific set of model assumptions (i.e., sensitive). Different models may result in marginal distributions that differ in position and/or shape for equivalent parameters, but we cannot tell from this alone which model better represents reality—this requires model selection approaches (2–4).Even for networks with relatively few nodes (corresponding to ODE models involving few interacting entities), the number of possible topologies can be enormous. Searching this “model space” presents formidable computational challenges. We use here a gradient-matching parameter inference approach that exploits the fact that the nth node, x_n, in our network representation is conditionally independent of all other nodes given its regulating parents, Pa(x_n) (20–26). The exploration of network topologies is then reduced to the much simpler problem of considering, independently for each n, the possible parent sets of x_n in an approach that is straightforwardly parallelized.We use biological examples to illustrate local and global searches of model spaces to identify alternative model structures that are consistent with available data. In some cases we find that even minor structural uncertainty in model topology can render our conclusions—here parameter inferences—unreliable and make PSA results positively misleading. However, other inferences are robust across diverse compatible model structures, allowing us to be more confident in assigning scientific meaning to the inferred parameter values.     

Dynamical facilitation governs glassy dynamics in suspensions of colloidal ellipsoids

One of the greatest challenges in contemporary condensed matter physics is to ascertain whether the formation of glasses from liquids is fundamentally thermodynamic or dynamic in origin. Although the thermodynamic paradigm has dominated theoretical research for decades, the purely kinetic perspective of the dynamical facilitation (DF) theory has attained prominence in recent times. In particular, recent experiments and simulations have highlighted the importance of facilitation using simple model systems composed of spherical particles. However, an overwhelming majority of liquids possess anisotropy in particle shape and interactions, and it is therefore imperative to examine facilitation in complex glass formers. Here, we apply the DF theory to systems with orientational degrees of freedom as well as anisotropic attractive interactions. By analyzing data from experiments on colloidal ellipsoids, we show that facilitation plays a pivotal role in translational as well as orientational relaxation. Furthermore, we demonstrate that the introduction of attractive interactions leads to spatial decoupling of translational and rotational facilitation, which subsequently results in the decoupling of dynamical heterogeneities. Most strikingly, the DF theory can predict the existence of reentrant glass transitions based on the statistics of localized dynamical events, called excitations, whose duration is substantially smaller than the structural relaxation time. Our findings pave the way for systematically testing the DF approach in complex glass formers and also establish the significance of facilitation in governing structural relaxation in supercooled liquids.The transformation of liquids into glasses is as ubiquitous as it is enigmatic. From the formation of obsidian during volcanic eruptions (1) and fabrication of superstrong metallic glasses (2) to exotic forms of slow dynamics in crystals of colloidal dimers (3) and Janus particles (4), glass formation pervades nature, industry, and academia. A vast majority of molecular glass-forming materials exhibit anisotropy in shape and interparticle interactions, which often has a profound influence on their glassy dynamics. The rapidly expanding repertoire of chemists has made it possible to design colloidal particles of desired shape and interactions that can serve as realistic experimental analogs of these molecular liquids (5). By contrast, prominent theories like the Adam–Gibbs (6) theory, random first-order transition (RFOT) theory (7, 8), and the dynamical facilitation (DF) theory (9, 10) have been tested predominantly on spherical glass formers with isotropic interactions, which exhibit gross features of glassy dynamics, but fail to capture the nuances of vitrification in complex systems.The discovery of growing static (11–16) and dynamic (17–21) length scales appears to support the thermodynamic perspective of the Adam–Gibbs and RFOT theories. However, the growth in static length scales over the dynamical range accessible to numerical simulations is often minuscule and much smaller than the corresponding growth in dynamic length scales (21, 22). This renders any causal connection between growing static length scales and growing timescales doubtful (22). Moreover, recent simulations (23) and colloid experiments (24) have shown that growing dynamical correlations in the form of string-like cooperative motion emerge naturally within the purely kinetic approach of the DF theory. To compound matters further, facilitation is present even within the RFOT framework, albeit as a consequence of slow dynamics rather than a cause (25). Thus, although DF has been shown to exist (23, 24, 26–29), its relative importance as a mechanism of structural relaxation is still debated (30–32). The application of the DF approach to complex glass formers will therefore not only enhance our understanding of glass transitions in these systems, but also help ascertain the relevance of facilitation in governing structural relaxation.Here, we apply the DF theory to elucidate glass formation in suspensions of colloidal ellipsoids with repulsive as well as attractive interactions. The DF theory claims that structural relaxation in glass-forming liquids proceeds via a process known as dynamical facilitation, whereby localized mobile regions, termed excitations, mediate motion in neighboring regions in a manner that conserves mobility (9, 10). We first show that the notions of localized excitations and facilitated dynamics can be extended even to orientational relaxation. Next, we demonstrate that the spatial decoupling of dynamical heterogeneities (DHs) observed in colloid experiments stems from the spatial decoupling of rotational and translational facilitation. Most importantly, the DF theory can predict the existence of recently observed reentrant glass transitions (33) from the density dependence of the concentration of excitations. Our findings not only highlight the importance of facilitated dynamics in anisotropic glass formers but also reinforce the claim that, in the broader context of the glass transition, facilitation dominates structural relaxation.     

连续性肾脏替代治疗中滤器后加热法对患者体温的影响

目的探讨连续性肾脏替代治疗(CRRT)中滤器后加热法对患者体温的影响。方法将60例行CRRT治疗的患者随机分为对照组和观察组各30例,对照组按常规将加温装置连接到置换液的管路上,观察组将加温装置连接到滤器后静脉端血液回输管路上。两组均于CRRT治疗开始3h、6h、12h时测量患者体温及深静脉置管的动、静脉端的血液温度;比较两组治疗前后溶血反应相关检验结果。结果在CRRT治疗不同时段,两组深静脉置管的动、静脉端的血液温度比较,干预主效应均P<0.05;治疗12h时观察组低体温发生率显著低于对照组(P<0.05)。两组治疗前后溶血反应相关检验结果比较,差异无统计学意义(均P>0.05)。结论滤器后加热血液回输管路的方式可安全有效地补充CRRT治疗中循环热量,降低CRRT治疗中低体温发生率。     

金属冠脉支架磁共振适用性检测方法

目的:建立金属冠脉支架磁共振适用性实验平台,以实验室测试为基础,研究金属冠脉支架的磁共振适用性。方法:金属冠脉支架磁共振适用性试验分为四个部分,在3T磁共振环境下,分别进行磁位移力试验、磁扭矩试验、致热试验和图像干扰试验。结果:退磁效果好的金属冠脉支架,磁位移力小于其自身重力,磁扭矩小于其自身重力扭矩,温度升高值小,图像畸变值小。退磁效果不好的支架,磁位移力大于其自身重力,温度升高多,图像畸变值大。结论:实验平台可以对金属冠脉支架磁共振适用性进行检测,并对其退磁效果进行评价。     

Role of the anterior insular cortex in integrative causal signaling during multisensory auditory–visual attention

Coordinated attention to information from multiple senses is fundamental to our ability to respond to salient environmental events, yet little is known about brain network mechanisms that guide integration of information from multiple senses. Here we investigate dynamic causal mechanisms underlying multisensory auditory–visual attention, focusing on a network of right‐hemisphere frontal–cingulate–parietal regions implicated in a wide range of tasks involving attention and cognitive control. Participants performed three ‘oddball’ attention tasks involving auditory, visual and multisensory auditory–visual stimuli during fMRI scanning. We found that the right anterior insula (rAI) demonstrated the most significant causal influences on all other frontal–cingulate–parietal regions, serving as a major causal control hub during multisensory attention. Crucially, we then tested two competing models of the role of the rAI in multisensory attention: an ‘integrated’ signaling model in which the rAI generates a common multisensory control signal associated with simultaneous attention to auditory and visual oddball stimuli versus a ‘segregated’ signaling model in which the rAI generates two segregated and independent signals in each sensory modality. We found strong support for the integrated, rather than the segregated, signaling model. Furthermore, the strength of the integrated control signal from the rAI was most pronounced on the dorsal anterior cingulate and posterior parietal cortices, two key nodes of saliency and central executive networks respectively. These results were preserved with the addition of a superior temporal sulcus region involved in multisensory processing. Our study provides new insights into the dynamic causal mechanisms by which the AI facilitates multisensory attention.