Opposing Responses to Scarcity Emerge from Functionally Unique Sociality Drivers.

AbstractFrom biofilms to whale pods, organisms across taxa live in groups, thereby accruing numerous diverse benefits of sociality. All social organisms, however, pay the inherent cost of increased resource competition. One expects that when resources become scarce, this cost will increase, causing group sizes to decrease. Indeed, this occurs in some species, but there are also species for which group sizes remain stable or even increase under scarcity. What accounts for these opposing responses? We present a conceptual framework, literature review, and theoretical model demonstrating that differing responses to sudden resource shifts can be explained by which sociality benefit exerts the strongest selection pressure on a particular species. We categorize resource-related benefits of sociality into six functionally distinct classes and model their effect on the survival of individuals foraging in groups under different resource conditions. We find that whether, and to what degree, the optimal group size (or correlates thereof) increases, decreases, or remains constant when resource abundance declines depends strongly on the dominant sociality mechanism. Existing data, although limited, support our model predictions. Overall, we show that across a wide diversity of taxa, differences in how group size shifts in response to resource declines can be driven by differences in the primary benefits of sociality.

Seasonal temperatures in West Antarctica during the Holocene.

The recovery of long-term climate proxy records with seasonal resolution is rare because of natural smoothing processes, discontinuities and limitations in measurement resolution. Yet insolation forcing, a primary driver of multimillennial-scale climate change, acts through seasonal variations with direct impacts on seasonal climate1. Whether the sensitivity of seasonal climate to insolation matches theoretical predictions has not been assessed over long timescales. Here, we analyse a continuous record of water-isotope ratios from the West Antarctic Ice Sheet Divide ice core to reveal summer and winter temperature changes through the last 11,000 years. Summer temperatures in West Antarctica increased through the early-to-mid-Holocene, reached a peak 4,100 years ago and then decreased to the present. Climate model simulations show that these variations primarily reflect changes in maximum summer insolation, confirming the general connection between seasonal insolation and warming and demonstrating the importance of insolation intensity rather than seasonally integrated insolation or season duration2,3. Winter temperatures varied less overall, consistent with predictions from insolation forcing, but also fluctuated in the early Holocene, probably owing to changes in meridional heat transport. The magnitudes of summer and winter temperature changes constrain the lowering of the West Antarctic Ice Sheet surface since the early Holocene to less than 162 m and probably less than 58 m, consistent with geological constraints elsewhere in West Antarctica4-7.

Detection of local mixing in time-series data using permutation entropy.

Mixing of neighboring data points in a sequence is a common, but understudied, effect in physical experiments. This can occur in the measurement apparatus (if material from multiple time points is pulled into a measurement chamber simultaneously, for instance) or the system itself, e.g., via diffusion of isotopes in an ice sheet. We propose a model-free technique to detect this kind of local mixing in time-series data using an information-theoretic technique called permutation entropy. By varying the temporal resolution of the calculation and analyzing the patterns in the results, we can determine whether the data are mixed locally, and on what scale. This can be used by practitioners to choose appropriate lower bounds on scales at which to measure or report data. After validating this technique on several synthetic examples, we demonstrate its effectiveness on data from a chemistry experiment, methane records from Mauna Loa, and an Antarctic ice core.

Toward automated extraction and characterization of scaling regions in dynamical systems.

Scaling regions-intervals on a graph where the dependent variable depends linearly on the independent variable-abound in dynamical systems, notably in calculations of invariants like the correlation dimension or a Lyapunov exponent. In these applications, scaling regions are generally selected by hand, a process that is subjective and often challenging due to noise, algorithmic effects, and confirmation bias. In this paper, we propose an automated technique for extracting and characterizing such regions. Starting with a two-dimensional plot-e.g., the values of the correlation integral, calculated using the Grassberger-Procaccia algorithm over a range of scales-we create an ensemble of intervals by considering all possible combinations of end points, generating a distribution of slopes from least squares fits weighted by the length of the fitting line and the inverse square of the fit error. The mode of this distribution gives an estimate of the slope of the scaling region (if it exists). The end points of the intervals that correspond to the mode provide an estimate for the extent of that region. When there is no scaling region, the distributions will be wide and the resulting error estimates for the slope will be large. We demonstrate this method for computations of dimension and Lyapunov exponent for several dynamical systems and show that it can be useful in selecting values for the parameters in time-delay reconstructions.

Using curvature to select the time lag for delay reconstruction.

We propose a curvature-based approach for choosing a good value for the time-delay parameter τ in delay reconstructions. The idea is based on the effects of the delay on the geometry of the reconstructions. If the delay is too small, the reconstructed dynamics are flattened along the main diagonal of the embedding space; too-large delays, on the other hand, can overfold the dynamics. Calculating the curvature of a two-dimensional delay reconstruction is an effective way to identify these extremes and to find a middle ground between them: both the sharp reversals at the extremes of an insufficiently unfolded reconstruction and the bends in an overfolded one create spikes in the curvature. We operationalize this observation by computing the mean Menger curvature of a trajectory segment on 2D reconstructions as a function of time delay. We show that the minimum of these values gives an effective heuristic for choosing the time delay. In addition, we show that this curvature-based heuristic is useful even in cases where the customary approach, which uses average mutual information, fails-e.g., noisy or filtered data.

An information-theoretic approach to extracting climate signals from deep polar ice cores.

Paleoclimate records are rich sources of information about the past history of the Earth system. Information theory provides a new means for studying these records. We demonstrate that weighted permutation entropy of water-isotope data from the West Antarctica Ice Sheet (WAIS) Divide ice core reveals meaningful climate signals in this record. We find that this measure correlates with accumulation (meters of ice equivalent per year) and may record the influence of geothermal heating effects in the deepest parts of the core. Dansgaard-Oeschger and Antarctic Isotope Maxima events, however, do not appear to leave strong signatures in the information record, suggesting that these abrupt warming events may actually be predictable features of the climate's dynamics. While the potential power of information theory in paleoclimatology is significant, the associated methods require well-dated and high-resolution data. The WAIS Divide core is the first paleoclimate record that can support this kind of analysis. As more high-resolution records become available, information theory could become a powerful forensic tool in paleoclimate science.

Social tipping points in animal societies.

Animal social groups are complex systems that are likely to exhibit tipping points-which are defined as drastic shifts in the dynamics of systems that arise from small changes in environmental conditions-yet this concept has not been carefully applied to these systems. Here, we summarize the concepts behind tipping points and describe instances in which they are likely to occur in animal societies. We also offer ways in which the study of social tipping points can open up new lines of inquiry in behavioural ecology and generate novel questions, methods, and approaches in animal behaviour and other fields, including community and ecosystem ecology. While some behaviours of living systems are hard to predict, we argue that probing tipping points across animal societies and across tiers of biological organization-populations, communities, ecosystems-may help to reveal principles that transcend traditional disciplinary boundaries.

Anatomy of leadership in collective behaviour.

Understanding the mechanics behind the coordinated movement of mobile animal groups (collective motion) provides key insights into their biology and ecology, while also yielding algorithms for bio-inspired technologies and autonomous systems. It is becoming increasingly clear that many mobile animal groups are composed of heterogeneous individuals with differential levels and types of influence over group behaviors. The ability to infer this differential influence, or leadership, is critical to understanding group functioning in these collective animal systems. Due to the broad interpretation of leadership, many different measures and mathematical tools are used to describe and infer "leadership," e.g., position, causality, influence, and information flow. But a key question remains: which, if any, of these concepts actually describes leadership? We argue that instead of asserting a single definition or notion of leadership, the complex interaction rules and dynamics typical of a group imply that leadership itself is not merely a binary classification (leader or follower), but rather, a complex combination of many different components. In this paper, we develop an anatomy of leadership, identify several principal components, and provide a general mathematical framework for discussing leadership. With the intricacies of this taxonomy in mind, we present a set of leadership-oriented toy models that should be used as a proving ground for leadership inference methods going forward. We believe this multifaceted approach to leadership will enable a broader understanding of leadership and its inference from data in mobile animal groups and beyond.

Anomaly Detection in Paleoclimate Records Using Permutation Entropy.

Permutation entropy techniques can be useful for identifying anomalies in paleoclimate data records, including noise, outliers, and post-processing issues. We demonstrate this using weighted and unweighted permutation entropy with water-isotope records containing data from a deep polar ice core. In one region of these isotope records, our previous calculations (See Garland et al. 2018) revealed an abrupt change in the complexity of the traces: specifically, in the amount of new information that appeared at every time step. We conjectured that this effect was due to noise introduced by an older laboratory instrument. In this paper, we validate that conjecture by reanalyzing a section of the ice core using a more advanced version of the laboratory instrument. The anomalous noise levels are absent from the permutation entropy traces of the new data. In other sections of the core, we show that permutation entropy techniques can be used to identify anomalies in the data that are not associated with climatic or glaciological processes, but rather effects occurring during field work, laboratory analysis, or data post-processing. These examples make it clear that permutation entropy is a useful forensic tool for identifying sections of data that require targeted reanalysis-and can even be useful for guiding that analysis.

Ablation as targeted perturbation to rewire communication network of persistent atrial fibrillation.

Persistent atrial fibrillation (AF) can be viewed as disintegrated patterns of information transmission by action potential across the communication network consisting of nodes linked by functional connectivity. To test the hypothesis that ablation of persistent AF is associated with improvement in both local and global connectivity within the communication networks, we analyzed multi-electrode basket catheter electrograms of 22 consecutive patients (63.5 ± 9.7 years, 78% male) during persistent AF before and after the focal impulse and rotor modulation-guided ablation. Eight patients (36%) developed recurrence within 6 months after ablation. We defined communication networks of AF by nodes (cardiac tissue adjacent to each electrode) and edges (mutual information between pairs of nodes). To evaluate patient-specific parameters of communication, thresholds of mutual information were applied to preserve 10% to 30% of the strongest edges. There was no significant difference in network parameters between both atria at baseline. Ablation effectively rewired the communication network of persistent AF to improve the overall connectivity. In addition, successful ablation improved local connectivity by increasing the average clustering coefficient, and also improved global connectivity by decreasing the characteristic path length. As a result, successful ablation improved the efficiency and robustness of the communication network by increasing the small-world index. These changes were not observed in patients with AF recurrence. Furthermore, a significant increase in the small-world index after ablation was associated with synchronization of the rhythm by acute AF termination. In conclusion, successful ablation rewires communication networks during persistent AF, making it more robust, efficient, and easier to synchronize. Quantitative analysis of communication networks provides not only a mechanistic insight that AF may be sustained by spatially localized sources and global connectivity, but also patient-specific metrics that could serve as a valid endpoint for therapeutic interventions.

Leveraging information storage to select forecast-optimal parameters for delay-coordinate reconstructions.

Delay-coordinate reconstruction is a proven modeling strategy for building effective forecasts of nonlinear time series. The first step in this process is the estimation of good values for two parameters, the time delay and the embedding dimension. Many heuristics and strategies have been proposed in the literature for estimating these values. Few, if any, of these methods were developed with forecasting in mind, however, and their results are not optimal for that purpose. Even so, these heuristics-intended for other applications-are routinely used when building delay coordinate reconstruction-based forecast models. In this paper, we propose an alternate strategy for choosing optimal parameter values for forecast methods that are based on delay-coordinate reconstructions. The basic calculation involves maximizing the shared information between each delay vector and the future state of the system. We illustrate the effectiveness of this method on several synthetic and experimental systems, showing that this metric can be calculated quickly and reliably from a relatively short time series, and that it provides a direct indication of how well a near-neighbor based forecasting method will work on a given delay reconstruction of that time series. This allows a practitioner to choose reconstruction parameters that avoid any pathologies, regardless of the underlying mechanism, and maximize the predictive information contained in the reconstruction.

Followers are not enough: a multifaceted approach to community detection in online social networks.

In online social media networks, individuals often have hundreds or even thousands of connections, which link these users not only to friends, associates, and colleagues, but also to news outlets, celebrities, and organizations. In these complex social networks, a 'community' as studied in the social network literature, can have very different meaning depending on the property of the network under study. Taking into account the multifaceted nature of these networks, we claim that community detection in online social networks should also be multifaceted in order to capture all of the different and valuable viewpoints of 'community.' In this paper we focus on three types of communities beyond follower-based structural communities: activity-based, topic-based, and interaction-based. We analyze a Twitter dataset using three different weightings of the structural network meant to highlight these three community types, and then infer the communities associated with these weightings. We show that interesting insights can be obtained about the complex community structure present in social networks by studying when and how these four community types give rise to similar as well as completely distinct community structure.

Modelling the heart as a communication system.

Electrical communication between cardiomyocytes can be perturbed during arrhythmia, but these perturbations are not captured by conventional electrocardiographic metrics. We developed a theoretical framework to quantify electrical communication using information theory metrics in two-dimensional cell lattice models of cardiac excitation propagation. The time series generated by each cell was coarse-grained to 1 when excited or 0 when resting. The Shannon entropy for each cell was calculated from the time series during four clinically important heart rhythms: normal heartbeat, anatomical reentry, spiral reentry and multiple reentry. We also used mutual information to perform spatial profiling of communication during these cardiac arrhythmias. We found that information sharing between cells was spatially heterogeneous. In addition, cardiac arrhythmia significantly impacted information sharing within the heart. Entropy localized the path of the drifting core of spiral reentry, which could be an optimal target of therapeutic ablation. We conclude that information theory metrics can quantitatively assess electrical communication among cardiomyocytes. The traditional concept of the heart as a functional syncytium sharing electrical information cannot predict altered entropy and information sharing during complex arrhythmia. Information theory metrics may find clinical application in the identification of rhythm-specific treatments which are currently unmet by traditional electrocardiographic techniques.

Prediction in projection.

Prediction models that capture and use the structure of state-space dynamics can be very effective. In practice, however, one rarely has access to full information about that structure, and accurate reconstruction of the dynamics from scalar time-series data-e.g., via delay-coordinate embedding-can be a real challenge. In this paper, we show that forecast models that employ incomplete reconstructions of the dynamics-i.e., models that are not necessarily true embeddings-can produce surprisingly accurate predictions of the state of a dynamical system. In particular, we demonstrate the effectiveness of a simple near-neighbor forecast technique that works with a two-dimensional time-delay reconstruction of both low- and high-dimensional dynamical systems. Even though correctness of the topology may not be guaranteed for incomplete reconstructions like this, the dynamical structure that they do capture allows for accurate predictions-in many cases, even more accurate than predictions generated using a traditional embedding. This could be very useful in the context of real-time forecasting, where the human effort required to produce a correct delay-coordinate embedding is prohibitive.

Model-free quantification of time-series predictability.

This paper provides insight into when, why, and how forecast strategies fail when they are applied to complicated time series. We conjecture that the inherent complexity of real-world time-series data, which results from the dimension, nonlinearity, and nonstationarity of the generating process, as well as from measurement issues such as noise, aggregation, and finite data length, is both empirically quantifiable and directly correlated with predictability. In particular, we argue that redundancy is an effective way to measure complexity and predictive structure in an experimental time series and that weighted permutation entropy is an effective way to estimate that redundancy. To validate these conjectures, we study 120 different time-series data sets. For each time series, we construct predictions using a wide variety of forecast models, then compare the accuracy of the predictions with the permutation entropy of that time series. We use the results to develop a model-free heuristic that can help practitioners recognize when a particular prediction method is not well matched to the task at hand: that is, when the time series has more predictive structure than that method can capture and exploit.

Wound complications associated with bone morphogenetic protein-2 in orthopaedic trauma surgery.

OBJECTIVE: To document the incidence of postoperative wound complications associated with the use of rhBMP-2 in a large series of patients for both acute traumatic and reconstructive extremity cases. DESIGN: Retrospective chart and radiographic review. SETTING: Level I trauma center. METHODS: A retrospective chart and x-ray review was performed on cases between 2002 and 2009, in which rhBMP-2 (Infuse) was used in acute trauma or posttraumatic reconstruction. The following data points were collected: age, surgical site, purpose (acute vs. reconstructive), associated wound factors (open fractures, soft tissue injury requiring coverage, or history of infection), signs of infection (seroma, erythema, prolonged drainage, abscess), reoperation rate secondary to wound complication, culture results, and union. These cases were then compared with a matched cohort without the use of bone morphogenetic protein-2 (matched for age, type of case, anatomic site, and open injury) for statistical analysis. RESULTS: Group 1 was comprised a total of 193 patients whose treatment included rhBMP-2 (155 reconstructive and 38 acute open fractures). Group 2 was comprised 181 patients treated without the use of rhBMP-2 (145 reconstructive and 36 acute open fractures). The incidences of documented wound complications were 31% (60/193) in group 1 and 18% (33/181) in group 2 (P = 0.004). Reoperation rates for wound complications were in 3.1% of group 1 and 8.3% of group 2 (P = 0.04). Age, sex, anatomic site, acute trauma, open fracture, and the need for soft tissue reconstruction did not correlate with the need to return to the operating room for presumed or actual wound infection. The rates of union between rhBMP-2 and control groups were 90% versus 74% (P < 0.001); for acute trauma cases, 94% versus 79% (P = 0.220); and for reconstructive cases, 89% versus 73% (P = 0.002). CONCLUSIONS: The use of rhBMP-2 in both acute traumatic and posttraumatic reconstructive extremity surgery may increase the incidence of prolonged postoperative serous wound drainage. However, this does not seem to correlate with an increased incidence of postoperative wound infection or the need for reoperation. The use of rhBMP-2 seems to have a beneficial effect in improving union rates for both acute trauma and posttraumatic reconstruction of the extremities (P = 0.002); however, this and the mechanism for prolonged serous drainage require further study before definitive recommendations can be made. LEVEL OF EVIDENCE: Therapeutic Level III. See Instructions for Authors for a complete description of levels of evidence.

Comparison of insertional trauma between suprapatellar and infrapatellar portals for tibial nailing.

The purpose of this study was to determine differences in insertional articular trauma in infrapatellar tibial portal and suprapatellar portal intramedullary tibial nail insertion techniques. A cadaveric study was performed on 10 matched pairs of fresh-frozen adult cadaver lower extremities with intact extensor mechanisms. Two study groups with 10 limbs each were created: left lower limbs were treated with a standard medial parapatellar nailing portal and right lower limbs were treated with a suprapatellar tibial nailing portal. Start points were created under fluoroscopic guidance in anteroposterior and mediolateral planes. A start wire was placed and opening reaming was performed on the specimens using instrumentation specific to the nailing portal. Specimens were then dissected by medial parapatellar arthrotomy, revealing the intra-articular condition of the knee structures. The border of the tibial entry reamer hole was measured to the anterior horns of the menisci, anterior cruciate ligament root, and intermeniscal ligament using a digital caliper accurate to 0.02 mm. The structure was considered damaged if the structure was obviously damaged on visual inspection or if a measurement was less than 1 mm. Impact to intra-articular structures was numerically lower in the suprapatellar group (2/10) compared with the infrapatellar group (4/10), but the difference was not statistically significant between the 2 groups (P=.629). The suprapatellar portal approach to the tibial start point demonstrated a lower overall incidence of damage to intra-articular structures, but no significant statistical difference existed between the 2 treatment groups.

Plaster: our orthopaedic heritage: AAOS exhibit selection.

BACKGROUND: Plaster has been used for centuries as a stiffening agent to treat fractures and other musculoskeletal conditions that require rest, immobilization, or correction of a deformity. Despite modern metallurgy and internal stabilization, plaster casts and splints remain an important means of external stabilization. Casting is a dying art as modern internal and external fixation replace external immobilization. Proper casting technique is paramount. This manuscript outlines the history and chemistry of immobilization materials and techniques as well as the differences among them and the advantages and disadvantages of each. METHODS: Historical references, peer-reviewed journals, textbooks, and primary sources were reviewed to provide data for this review. RESULTS: The history of immobilization reveals a progressive development and refinement of materials that culminated in Mathijsen's plaster bandage in 1851. In 1798, calcium sulfate (plaster of Paris) was introduced. By 1927, crinoline rolls dipped in plaster treated with binding agents facilitated application. Synthetic casting "tapes" (45% polyurethane resin and 55% fiberglass) were introduced in the 1970s. Splinting techniques are ancient, with development spurred by treatment of war wounds. Plaster relies on soft-tissue contact to maintain rigidity. There are well-known advantages, disadvantages, and complications of plaster management. Casting materials all create an exothermic reaction. Burns are associated with water temperatures of >24°C, more than eight layers (ply), and inadequate ventilation. The maximum water temperature must be lower with fiberglass casts. Plaster was the definitive management for most fractures for over 100 years until it was replaced by modern surgical techniques involving internal fixation in the latter part of the twentieth century. CONCLUSIONS: Plaster casts and splints remain an important treatment method for acute and chronic orthopaedic conditions.

Iterated function system models in data analysis: detection and separation.

We investigate the use of iterated function system (IFS) models for data analysis. An IFS is a discrete-time dynamical system in which each time step corresponds to the application of one of the finite collection of maps. The maps, which represent distinct dynamical regimes, may be selected deterministically or stochastically. Given a time series from an IFS, our algorithm detects the sequence of regime switches under the assumption that each map is continuous. This method is tested on a simple example and an experimental computer performance data set. This methodology has a wide range of potential uses: from change-point detection in time-series data to the field of digital communications.

The delta opioid receptor agonist, SNC80, has complex, dose-dependent effects on pilocarpine-induced seizures in Sprague-Dawley rats.

Delta opioid receptor (DOR) selective agonists hold promise clinically as analgesics, but their effects on seizures remain controversial. In this study we examined the effects of the DOR agonist, (+)-4-[(alpha R)-alpha-((2S,5R)-4-allyl-2,5-dimethyl-1-piperazinyl)-3-methoxybenzyl]-N,N-diethyl-benzamide (SNC80), on behavioral seizures and hippocampal histopathology in the pilocarpine model of temporal lobe epilepsy. Systemic administration of SNC80 (30 or 60 mg/kg) alone elicited brief seizures within minutes of injection in about half of all rats. When SNC80 (30 or 60 mg/kg) was given prior to pilocarpine administration, trends toward increased latencies to first seizure and status epilepticus (SE) were seen, which correlated with the incidence of a prior, brief SNC80-induced seizure. Significant dose-dependent effects of SNC80 also were observed. Prior administration of SNC80 (30 mg/kg) significantly decreased the number of rats exhibiting acute pilocarpine-induced seizures and overall seizure severity compared to rats given pilocarpine alone, suggesting that SNC80 was anticonvulsant. SNC80 (60 mg/kg) also decreased overall seizure severity. However, SNC80 (60 mg/kg) doubled the total seizure time and the number of rats exhibiting prolonged SE compared to pilocarpine alone, further suggesting that SNC80 has pro-convulsant properties. Significant effects of SNC80 on pilocarpine-induced seizures did not correlate with the occurrence of a prior SNC80-induced seizure. The degree of hilar neuron loss and mossy fiber sprouting correlated strongly with prolonged SE rather than dose of SNC80 (> or =60 min), suggesting that SNC80 did not dramatically alter pilocarpine-induced seizures in the absence of behavioral modifications. Our results demonstrate that the DOR agonist, SNC80, has complex, dose-dependent effects on pilocarpine-induced seizures.