Dynamics Of Components Research Articles

The migration component in the demographic development of Russia in the post-war period (1951–2020)

The article discusses possible combinations of the impact on the dynamics of the population of migration growth (decline) and natural decline (increase). Variants of combinations with the corresponding values of natural and migration movements are shown using examples of the demographic dynamics of Russia. Reliable information about the migration movement of the population refers only to the time that began in the 50s. It is distributed over periods that differ in the nature of the impact of the reproductive and migration components on demographic dynamics. During these periods spanning seventy years, the country's population increased by almost 44 million. In the first 25 years, there was a migration decline, more than offset by natural population growth. Then migration, along with natural movement, acted as a component of population dynamics. Due to natural growth, the population increased to the 1951 level. by 33.8 million people. The migration component accounted for 10.6 million people. Their ratio was 3/4 to 1/4. It is shown that in the second half of the tenth years of the twenty-first century, Russia entered a difficult demographic time for it, aggravated by the fact that by now in the new abroad the migration potential oriented towards Russia has significantly decreased

Environmental and behavioral factors influencing individual variation in spatial use by Guiana dolphins (Sotalia guianensis)

AbstractMarine apex predators, such as coastal dolphins, are key components of ecosystem function and dynamics; understanding their spatial use therefore is important for conservation planning. Here, we describe spatial use of the threatened Guiana dolphin, Sotalia guianensis, within a mosaic of protected areas at their southern range in the Paranaguá estuarine complex (PEC), Brazil. Based on environmental characteristics and S. guianensis population densities, individual photo-identification surveys were focused on two sectors of the northern mouth of the PEC between 2013 and 2015. We photo-identified 107 individuals and recorded 351 groups of Guiana dolphins; nearly 50% of which contained calves. Group sizes were small (mean ± SD: Sector 1 = 3.4 ± 2.7; Sector 2 = 3.6 ± 2.0) and ranged from 1 to 30 individuals. Clustering algorithms were applied to resighted, photo-identified Guiana dolphins to classify individuals as “residents” (sighted in 87% of surveys, n = 11) and “transients” (two clusters; 62%, n = 22; 95%, n = 72). All individuals predominantly were observed foraging (90% in Sector 1 and 76% in Sector 2), but spatial use by transient individuals differed from the residents in terms of environmental (slope, water depth, and rainfall) and behavioral factors (presence of calves and foraging behavior). Compared to transients, resident dolphins ranged over smaller areas, typically more suitable for feeding, calf caring, and more protected from predators and vessels. Because resident Guiana dolphins exhibit small home ranges, they are particularly vulnerable to local anthropogenic activities and habitat loss. These characteristics imply a strong need for regionalized management plans to reduce negative impacts and to maintain the ecological functionality of the species.

HGG-21. MALIGNANT SYNAPTIC PLASTICITY IN PEDIATRIC HIGH-GRADE GLIOMAS

Pediatric high-grade gliomas (pHGG) are a devastating group of diseases that urgently require novel therapeutic options. We have previously demonstrated that pHGGs directly synapse onto neurons and the subsequent tumor cell depolarization, mediated by calcium-permeable AMPA channels, promotes their proliferation. The regulatory mechanisms governing these postsynaptic connections are unknown. Here, we investigated the role of BDNF-TrkB signaling in modulating the plasticity of the malignant synapse. BDNF ligand activation of its canonical receptor, TrkB (which is encoded for by the gene NTRK2), has been shown to be one important modulator of synaptic regulation in the normal setting. Electrophysiological recordings of glioma cell membrane properties, in response to acute neurotransmitter stimulation, demonstrate in an inward current resembling AMPA receptor (AMPAR) mediated excitatory neurotransmission. Extracellular BDNF increases the amplitude of this glutamate-induced tumor cell depolarization and this effect is abrogated in NTRK2 knockout glioma cells. Upon examining tumor cell excitability using in situ calcium imaging, we found that BDNF increases the intensity of glutamate-evoked calcium transients in GCaMP6s expressing glioma cells. Western blot analysis indicates the tumors AMPAR properties are altered downstream of BDNF induced TrkB activation in glioma. We find that BDNF-TrkB signaling promotes neuron-to-glioma synaptogenesis as measured by high-resolution confocal and electron microscopy in culture and tumor xenografts. Our analysis of published pHGG transcriptomic datasets, together with brain slice conditioned medium experiments in culture, indicate the tumor microenvironment as the chief source of BDNF ligand. Disruption of the BDNF-TrkB pathway in patient-derived orthotopic glioma xenograft models, both genetically and pharmacologically, results in an increased overall survival and reduced tumor proliferation rate. These findings suggest that gliomas leverage mechanisms of plasticity to modulate the excitatory channels involved in synaptic neurotransmission and they reveal the potential to target the regulatory components of glioma circuit dynamics as a therapeutic strategy for these lethal cancers.

Intraseasonal Variability of Sea Level in the Western North Pacific

AbstractSea levels in the Western North Pacific (WNP) are presented with anomalous intraseasonal variations. This study examines the response of sea level in the WNP to the atmospheric Intraseasonal Oscillation modes, namely the Madden–Julian Oscillation (MJO) and the Boreal Summer Intraseasonal Oscillation (BSISO), using the 25 years (1993–2017) of satellite altimetry and barotropic model output. In winter, the MJO has significant effects on the component of sea level due to the instant wind and atmospheric pressure effects (high‐frequency), showing an eastward propagation pattern in most regions, with the strongest effects in the western marginal seas. The MJO‐associated pattern of dynamical (low‐frequency) component of sea level propagates southward, with the significant effects mostly in the tropics. In summer, the BSISO‐associated pattern of the high‐frequency component of sea level moves from southwest to northeast, with the largest anomalies in the middle of WNP (20°N‐30°N), while the strongest BSISO effects on the low‐frequency component are detectable mostly in the coasts of China and east of the Philippines. The MJO and BSISO can also modulate the probability of extreme sea level events. In winter, during phases 2–5, MJO increases the chance of extreme high events in the high‐frequency component of sea level by 100%–200% in the western coasts and the tropics. In summer, in BSISO phases 6–7, the chance of extreme high events in the high‐frequency component of sea level is enhanced by >300% in the South China Sea and east of the Philippines.

Conceptualizing multilevel research designs of resilience.

Resilience is a broad concept allowing us to understand health and well-being as a multidimensional process that continually grapples with a multitude of stressors. Currently, there are efforts across disciplines and scales to develop this concept of resilience. Unfortunately, individual and community resilience efforts tend to only abstractly conceptualize macroscale dynamics while social-ecological efforts tend to treat individuals and communities as nonindependent components of these macroscale dynamics. Combining these efforts is needed to create a robust dialog around resilience. This paper reviews and synthesizes social-ecological, community, and individual resilience literature by proposing longitudinal, multilevel models of resilience. In developing these models, some of the issues that have prevented synthesizing these literatures are resolved, including generalizability issues, within system variation, and the operationalization of social and natural and micro- and macroscale factors. Three brief examples are presented to elaborate on the utility of the multilevel model of resilience.

Drift Indices Confirm That Rapid Larval Displacement Is Essential for Recruitment Success in High-Latitude Oceans

Larval drift is a key process for successful fish recruitment. We used Norwegian spring-spawning herring (Clupea harengus) as model species to investigate the relationship between larval drift and recruitment. Larval drift indices were derived from simulations based on survey observations between 1993 and 2016. We show that forward simulated larval drift indices have an important positive relation to recruitment success. The relationship demonstrates elevated recruitment when larvae relocate rapidly northwards toward the Barents Sea. Negative or low larval drift indices coincide with only weak recruitment emphasizing limited survival in years with enhanced larval retention. Hence, with this work we combine drift model outcomes refined with survey data indicating that more extensive larval drift is an important component in population dynamics for high-latitude small pelagic fishes. However, larval displacement alone represents only one among many controlling factors but may offer possible predictions of the probability of higher or lower recruitment in the short term. The applicability of the drift indices is adaptable in all world oceans and all marine organisms that occupy planktonic life stages exposed to dynamic ocean currents. The study demonstrates how larval drift indices help to identify larval transport or retention to be crucial for population replenishment.

Nonthermal excitation effects mediated by sub-terahertz radiation on hydrogen exchange in ubiquitin

Water dynamics in the hydration layers of biomolecules play crucial roles in a wide range of biological functions. A hydrated protein contains multiple components of diffusional and vibrational dynamics of water and protein, which may be coupled at ∼0.1-THz frequency (10-ps timescale) at room temperature. However, the microscopic description of biomolecular functions based on various modes of protein-water-coupled motions remains elusive. A novel approach for perturbing the hydration dynamics in the subterahertz frequency range and probing them at the atomic level is therefore warranted. In this study, we investigated the effect of klystron-based, intense 0.1-THz excitation on the slow dynamics of ubiquitin using NMR-based measurements of hydrogen-deuterium exchange. We demonstrated that the subterahertz irradiation accelerated the hydrogen-deuterium exchange of the amides located in the interior of the protein and hydrophobic surfaces while decelerating this exchange in the amides located in the surface loop and short 310 helix regions. This subterahertz-radiation-induced effect was qualitatively contradictory to the increased-temperature-induced effect. Our results suggest that the heterogeneous water dynamics occurring at the protein-water interface include components that are nonthermally excited by the subterahertz radiation. Such subterahertz-excited components may be linked to the slow function-related dynamics of the protein.

Seed bank persistence of a South American cordgrass in invaded northern Atlantic and Pacific Coast estuaries.

Invasive alien plant species impart considerable impacts that contribute to the decline of biodiversity worldwide. The ability of an invasive species to overcome barriers to establish and spread in new environments, and the long-term effects of plant invasions supporting their persistence are keys to invasion success. The capacity of introduced species to form soil seed banks can contribute to their invasiveness, yet few studies of invaders have addressed seed bank dynamics. Improved knowledge of this recruitment process can improve conservation management. We studied temporal and spatial changes in soil seed bank characteristics of the cordgrass Spartina densiflora from two continental invaded ranges. In the Odiel Marshes (Southwest Iberian Peninsula), S. densiflora formed transient seed banks (<1 year). At Humboldt Bay Estuary (California), viable seeds persisted for at least 4 years though the germination percentage fell abruptly after the first year from 29 % to less than 5 % of remaining viable seeds. Total soil seed bank density increased with S. densiflora above-ground cover in both estuaries, pointing to the transient component of the seed bank as a critical component of vegetation dynamics during S. densiflora invasion. Even so, seed densities as high as c. 750 seeds m-2 in Odiel Marshes and c. 12 400 seeds m-2 in Humboldt Bay were recorded in some plots without fruiting S. densiflora plants. S. densiflora spikelet (dispersal unit) density was more than double close to the sediment surface than deeper within soil. Our study shows the importance of evaluating seed banks during the design of invasive species management since seed bank persistence may vary among invaded sites, and can affect the timing and duration required for desired management outcomes.

Prognosis of Dynamical System Components with Varying Degradation Patterns using model–data–fusion

Failure prognosis is used to predict the future degradation and remaining useful life (RUL) of components. However, identification of future degradation and RUL of components is challenging when similar components in the same or different working conditions show varying degradation patterns. This is again more challenging when the component shows highly nonlinear degradation behavior, which may lead to erroneous RUL prediction and wrong prognosis. In this paper, a hybrid approach with the fusion of model–based and data–driven approaches is developed for the prognosis of dynamical system components whose degradations may follow different nonlinear trends. Here, degradation levels of different components are identified by using bond graph model–based distributed prognosis approach. However, artificial neural network based degradation models learned from the run–to–failure data of the components are used to predict the future degradation patterns and RUL of the components. The developed hybrid approach is applied to an electronic circuit test bed.

Bacterial diversity assessment of world's largest sewage-fed fish farms with special reference to water quality: a Ramsar site.

Bacterial community structure is one of the essential components of aquaculture dynamics and plays an important role in maintaining wetland health. The present work is an effort to study the structure of bacterial communities in the world's largest sewage-fed fish farms, the East Kolkata Wetlands (EKWs), along with their predicted functional metabolic pathways and correlation with environmental variables. Sequencing data analysis revealed the abundance of genera such as Arcobacter (0-50%), Pseudomonas (0-15%), Sulfurospirillum (0-9%), Cloacibacterium (0-6%), hgcI clade (7-29%), C39 (0-9%), V6 (3-36%), Fluiivicola (1-6%) and Cyanobium (3-8%) in the EKWs. Further, water quality analysis of three treatment groups, i.e. Sewage, Sewage F-1 and Sewage F-2, revealed that dissolved oxygen (DO), biochemical oxygen demand (BOD) and chemical oxygen demand (COD) differed significantly and violated the standard prescribed norms (Central Pollution Control Board, CPCB, New Delhi) for fishery propagation and irrigation in India. Further, the correlation matrix analysis between the abundance of bacterial genera and environmental variables indicated that DO, BOD and COD were mainly responsible for bacterial community structure and their proliferation in the EKWs. Our results indicated that the abundance of genera such as Arcobacter, Pseudomonas, Sulfurospirillum and Cloacibacterium has an inverse relationship with BOD and COD. Our observations based on the bacterial community structure and deteriorated water quality indicate the ineffective functioning and poor management of this man-made constructed wetland.

Reservoir Computing as a Tool for Climate Predictability Studies

AbstractReduced‐order dynamical models play a central role in developing our understanding of predictability of climate irrespective of whether we are dealing with the actual climate system or surrogate climate models. In this context, the linear inverse modeling (LIM) approach, by capturing a few essential interactions between dynamical components of the full system, has proven valuable in providing insights into predictability of the full system. We demonstrate that reservoir computing (RC), a form of learning suitable for systems with chaotic dynamics, provides an alternative nonlinear approach that improves on the predictive skill of the LIM approach. We do this in the example setting of predicting sea surface temperature in the North Atlantic in the preindustrial control simulation of a popular earth system model, the Community Earth System Model so that we can compare the performance of the new RC‐based approach with the traditional LIM approach both when learning data are plentiful and when such data are more limited. The improved predictive skill of the RC approach over a wide range of conditions—larger number of retained EOF coefficients, extending well into the limited data regime, etc.—suggests that this machine‐learning technique may have a use in climate predictability studies. While the possibility of developing a climate emulator—the ability to continue the evolution of the system on the attractor long after failing to be able to track the reference trajectory—is demonstrated in the Lorenz‐63 system, it is suggested that further development of the RC approach may permit such uses of the new approach in more realistic predictability studies.

Assessing Different Temporal Scales of Calcium Dynamics in Networks of Beta Cell Populations.

Beta cells within the pancreatic islets of Langerhans respond to stimulation with coherent oscillations of membrane potential and intracellular calcium concentration that presumably drive the pulsatile exocytosis of insulin. Their rhythmic activity is multimodal, resulting from networked feedback interactions of various oscillatory subsystems, such as the glycolytic, mitochondrial, and electrical/calcium components. How these oscillatory modules interact and affect the collective cellular activity, which is a prerequisite for proper hormone release, is incompletely understood. In the present work, we combined advanced confocal Ca2+ imaging in fresh mouse pancreas tissue slices with time series analysis and network science approaches to unveil the glucose-dependent characteristics of different oscillatory components on both the intra- and inter-cellular level. Our results reveal an interrelationship between the metabolically driven low-frequency component and the electrically driven high-frequency component, with the latter exhibiting the highest bursting rates around the peaks of the slow component and the lowest around the nadirs. Moreover, the activity, as well as the average synchronicity of the fast component, considerably increased with increasing stimulatory glucose concentration, whereas the stimulation level did not affect any of these parameters in the slow component domain. Remarkably, in both dynamical components, the average correlation decreased similarly with intercellular distance, which implies that intercellular communication affects the synchronicity of both types of oscillations. To explore the intra-islet synchronization patterns in more detail, we constructed functional connectivity maps. The subsequent comparison of network characteristics of different oscillatory components showed more locally clustered and segregated networks of fast oscillatory activity, while the slow oscillations were more global, resulting in several long-range connections and a more cohesive structure. Besides the structural differences, we found a relatively weak relationship between the fast and slow network layer, which suggests that different synchronization mechanisms shape the collective cellular activity in islets, a finding which has to be kept in mind in future studies employing different oscillations for constructing networks.

Some open problems in low dimensional dynamical systems

The aim of this paper is to share with the mathematical community a list of 33 problems that I have found along the years in my research. I believe that it is worth to think about them and, hopefully, solve some of the problems or make some substantial progress. Many of them are about planar differential equations but there are also questions about other mathematical aspects: Abel differential equations, difference equations, global asymptotic stability, geometrical questions, problems involving polynomials and some recreational problems with a dynamical component.

Pneumococcal Competition Modulates Antibiotic Resistance in the Pre-Vaccination Era: A Modelling Study.

The ongoing emergence of antibiotic resistant strains and high frequencies of antibiotic resistance of Streptococcus pneumoniae poses a major public health challenge. How and which ecological and evolutionary mechanisms maintain the coexistence of antibiotic resistant and susceptible strains remains largely an open question. We developed an individual-based, stochastic model expanding on a previous pneumococci modelling framework. We explore how between- and within-host mechanisms of competition can sustain observed levels of resistance to antibiotics in the pre-vaccination era. Our framework considers that within-host competition for co-colonization between resistant and susceptible strains can arise via pre-existing immunity (immunological competition) or intrinsic fitness differences due to resistance costs (ecological competition). We find that beyond stochasticity, population structure or movement, competition at the within-host level can explain observed resistance frequencies. We compare our simulation results to pneumococcal antibiotic resistance data in the European region using approximate Bayesian computation. Our results demonstrate that ecological competition for co-colonization can explain the variation in co-existence of resistant and susceptible pneumococci observed in the pre-vaccination era. Furthermore, we show that within-host pneumococcal competition can facilitate the maintenance of resistance in the pre-vaccination era. Accounting for these competition-related components of pneumococcal dynamics can improve our understanding of drivers for the emergence and maintenance of antibiotic resistance in pneumococci.

Prefledging Growth and Recruitment of Female Lesser Scaup

ABSTRACTAnnual variation in juvenile recruitment is an important component of duck population dynamics, yet little is known about the factors affecting the probability of surviving and breeding in the first year of life. Two hypothesized mechanisms to explain annual variability are indirect carry‐over effects (COEs) from conditions experienced during the prefledging period and direct effects from climatic conditions during the postfledging period. We used Cormack‐Jolly‐Seber models to estimate apparent survival and detection rates of 643 juvenile female lesser scaup (Aythya affinis) marked just prior to fledging at Red Rock Lakes National Wildlife Refuge in southwestern Montana, USA, 2010–2018. We evaluated COEs from hatch date, a hatch date × spring phenology interaction, and conspecific duckling density in addition to a direct climatic effect of winter conditions (indexed by the El Niño Southern Oscillation [ENSO]) and spring habitat conditions on the study area. We used growth data from a subset (n = 190) of known‐aged ducklings to estimate the influence of hatch date and conspecific density on prefledging growth to help identify mechanisms underlying COEs. Prefledging growth and juvenile apparent survival were negatively related to measures of conspecific duckling density. We found evidence that detection probability varied annually for juvenile (but not adult) scaup, possibly representing decisions to delay breeding and not return to or remain at the study site in their first year of life. Like with apparent survival, there was suggestive evidence that detection probability decreased with increasing duckling density in the previous year. Hatching date was weakly negatively related to detection probability, but unrelated to apparent survival, whereas neither vital rate was related to winter ENSO index. Our results are consistent with a process where density‐dependent growth rates in the prefledging period carry over to influence fitness in subsequent life‐cycle stages. If this pattern generalizes to other systems, this density COE may have important implications for our understanding of duck population dynamics and reaffirms the importance of maintaining abundant brood‐rearing habitats in conservation and management of ducks. © 2021 The Wildlife Society.

Machine learning-based classification of mitochondrial morphology in primary neurons and brain

The mitochondrial network continually undergoes events of fission and fusion. Under physiologic conditions, the network is in equilibrium and is characterized by the presence of both elongated and punctate mitochondria. However, this balanced, homeostatic mitochondrial profile can change morphologic distribution in response to various stressors. Therefore, it is imperative to develop a method that robustly measures mitochondrial morphology with high accuracy. Here, we developed a semi-automated image analysis pipeline for the quantitation of mitochondrial morphology for both in vitro and in vivo applications. The image analysis pipeline was generated and validated utilizing images of primary cortical neurons from transgenic mice, allowing genetic ablation of key components of mitochondrial dynamics. This analysis pipeline was further extended to evaluate mitochondrial morphology in vivo through immunolabeling of brain sections as well as serial block-face scanning electron microscopy. These data demonstrate a highly specific and sensitive method that accurately classifies distinct physiological and pathological mitochondrial morphologies. Furthermore, this workflow employs the use of readily available, free open-source software designed for high throughput image processing, segmentation, and analysis that is customizable to various biological models.

Model Uncertainty in the Projected Indian Summer Monsoon Precipitation Change under Low-Emission Scenarios

The projected ISM precipitation changes under low-emission scenarios, Representative Concentration Pathway 2.6 (RCP2.6) and Shared Socioeconomic Pathway 1-2.6 (SSP1-2.6), are investigated by outputs from models participating in phases 5 and 6 of the Coupled Model Intercomparison Project (CMIP5 and CMIP6). Based on the high-emission scenarios like RCP8.5, the Intergovernmental Panel on Climate Change Fifth Assessment Report suggests a wetter Indian summer monsoon (ISM) by the end of 21st century. Although the multi-model ensemble mean (MME) ISM precipitation under RCP2.6 and SSP1-2.6 is still projected to increase over 2050–2099 referenced to 1900–1949, the intermodel spread of the ISM precipitation change is tremendous in both CMIPs. Indeed, the signal-to-noise ratio (SNR) of ISM precipitation change, defined as the MME divided by its intermodel standard deviation, is even below 1 under the low-emission scenarios. This casts doubts on a future wetter ISM in a warmer climate. Moisture budget analyses further show that most of the model uncertainty in ISM precipitation change is caused by its dynamical component from the atmospheric circulation change. As expected, the interhemispheric surface warming contrast is essential in causing the intermodel differences in ISM circulation and precipitation changes under low-emission scenarios. In addition, the projected wetter ISM is prominently enhanced from CMIP5 to CMIP6, along with reduced model uncertainty. However, the resultant increased SNR in CMIP6 is still low in most ISM regions. The results imply that ISM precipitation change is highly uncertain under low-emission scenarios, which greatly challenges the decisions-making in adaptation policies for the densely populated South Asian countries.

Phase transitions and stability of dynamical processes on hypergraphs

Hypergraphs naturally represent higher-order interactions, which persistently appear in social interactions, neural networks, and other natural systems. Although their importance is well recognized, a theoretical framework to describe general dynamical processes on hypergraphs is not available yet. In this paper, we derive expressions for the stability of dynamical systems defined on an arbitrary hypergraph. The framework allows us to reveal that, near the fixed point, the relevant structure is a weighted graph-projection of the hypergraph and that it is possible to identify the role of each structural order for a given process. We analytically solve two dynamics of general interest, namely, social contagion and diffusion processes, and show that the stability conditions can be decoupled in structural and dynamical components. Our results show that in social contagion process, only pairwise interactions play a role in the stability of the absorbing state, while for the diffusion dynamics, the order of the interactions plays a differential role. Our work provides a general framework for further exploration of dynamical processes on hypergraphs.

Twofold Sliding Controller Design for Uncertain Switched Nonlinear Systems

This paper deals with the problem of controller design for normal nonlinear switched systems with unknown dynamics. The situation assumes that the nonlinear component of the system dynamics is unknown and there are extra uncertain terms and external perturbations affecting the system responses. Unlike previous methods that usually apply artificial neural networks to obtain an approximate model for an unknown dynamics, we use simple adaptive laws to surmount such fluctuations as well as to circumvent the steady state errors in the approaches provided by neural networks or fuzzy logics. No information on the bounds and parameters of the uncertain parts is needed. We design a twofold sliding mode control methodology to stabilize the whole switched system with arbitrary switching signals. To handle the consequences of the inherent chattering, a smooth integral type control signal is constructed. It is proved that once the system states attain the developed sliding manifold, the system becomes insensitive to both the lumped uncertainties and the dynamics of the switched system. The common assumption of a known Lyapunov functions for the subsystems is relaxed and the derived adaptive approach provides nonconservative circumstances for guaranteeing the global stability of the equilibrium state. The theoretical results of this paper are generalized for the canonical nonlinear systems with mismatched uncertainties and a novel adaptive sliding manifold is proposed to suppress the effects of the system mismatching uncertain parts. A comparative computer simulation is developed to examine the effective performance of the introduced controllers.

A Recurrent Neural Network for Attenuating Non-cognitive Components of Pupil Dynamics.

There is increasing interest in how the pupil dynamics of the eye reflect underlying cognitive processes and brain states. Problematic, however, is that pupil changes can be due to non-cognitive factors, for example luminance changes in the environment, accommodation and movement. In this paper we consider how by modeling the response of the pupil in real-world environments we can capture the non-cognitive related changes and remove these to extract a residual signal which is a better index of cognition and performance. Specifically, we utilize sequence measures such as fixation position, duration, saccades, and blink-related information as inputs to a deep recurrent neural network (RNN) model for predicting subsequent pupil diameter. We build and evaluate the model for a task where subjects are watching educational videos and subsequently asked questions based on the content. Compared to commonly-used models for this task, the RNN had the lowest errors rates in predicting subsequent pupil dilation given sequence data. Most importantly was how the model output related to subjects' cognitive performance as assessed by a post-viewing test. Consistent with our hypothesis that the model captures non-cognitive pupil dynamics, we found (1) the model's root-mean square error was less for lower performing subjects than for those having better performance on the post-viewing test, (2) the residuals of the RNN (LSTM) model had the highest correlation with subject post-viewing test scores and (3) the residuals had the highest discriminability (assessed via area under the ROC curve, AUC) for classifying high and low test performers, compared to the true pupil size or the RNN model predictions. This suggests that deep learning sequence models may be good for separating components of pupil responses that are linked to luminance and accommodation from those that are linked to cognition and arousal.