Systems immunology of regulatory T cells: can one circuit explain it all?

Regulatory T (Treg) cells play vital roles in immune homeostasis and response, including discrimination between self- and non-self-antigens, containment of immunopathology, and inflammation resolution. These diverse functions are orchestrated by cellular circuits involving Tregs and other cell types across space and time. Despite dramatic progress in our understanding of Treg biology, a quantitative framework capturing how Treg-containing circuits give rise to these diverse functions is lacking. Here, we propose that different facets of Treg function can be interpreted as distinct operating regimes of the same underlying circuit. We discuss how a systems immunology approach, involving quantitative experiments, computational modeling, and machine learning, can advance our understanding of Treg function, and help identify general operating and design principles underlying immune regulation.

Constraining nonlinear time series modeling with the metabolic theory of ecology.

Forecasting the response of ecological systems to environmental change is a critical challenge for sustainable management. The metabolic theory of ecology (MTE) posits scaling of biological rates with temperature, but it has had limited application to population dynamic forecasting. Here we use the temperature dependence of the MTE to constrain empirical dynamic modeling (EDM), an equation-free nonlinear machine learning approach for forecasting. By rescaling time with temperature and modeling dynamics on a "metabolic time step," our method (MTE-EDM) improved forecast accuracy in 18 of 19 empirical ectotherm time series (by 19% on average), with the largest gains in more seasonal environments. MTE-EDM assumes that temperature affects only the rate, rather than the form, of population dynamics, and that interacting species have approximately similar temperature dependence. A review of laboratory studies suggests these assumptions are reasonable, at least approximately, though not for all ecological systems. Our approach highlights how to combine modern data-driven forecasting techniques with ecological theory and mechanistic understanding to predict the response of complex ecosystems to temperature variability and trends.

Dryland sensitivity to climate change and variability using nonlinear dynamics.

Primary productivity response to climatic drivers varies temporally, indicating state-dependent interactions between climate and productivity. Previous studies primarily employed equation-based approaches to clarify this relationship, ignoring the state-dependent nature of ecological dynamics. Here, using 40 y of climate and productivity data from 48 grassland sites across Mongolia, we applied an equation-free, nonlinear time-series analysis to reveal sensitivity patterns of productivity to climate change and variability and clarify underlying mechanisms. We showed that productivity responded positively to annual precipitation in mesic regions but negatively in arid regions, with the opposite pattern observed for annual mean temperature. Furthermore, productivity responded negatively to decreasing annual aridity that integrated precipitation and temperature across Mongolia. Productivity responded negatively to interannual variability in precipitation and aridity in mesic regions but positively in arid regions. Overall, interannual temperature variability enhanced productivity. These response patterns are largely unrecognized; however, two mechanisms are inferable. First, time-delayed climate effects modify annual productivity responses to annual climate conditions. Notably, our results suggest that the sensitivity of annual productivity to increasing annual precipitation and decreasing annual aridity can even be negative when the negative time-delayed effects of annual precipitation and aridity on productivity prevail across time. Second, the proportion of plant species resistant to water and temperature stresses at a site determines the sensitivity of productivity to climate variability. Thus, we highlight the importance of nonlinear, state-dependent sensitivity of productivity to climate change and variability, accurately forecasting potential biosphere feedback to the climate system.

A hybrid empirical and parametric approach for managing ecosystem complexity: Water quality in Lake Geneva under nonstationary futures.

Severe deterioration of water quality in lakes, characterized by overabundance of algae and declining dissolved oxygen in the deep lake (DOB), was one of the ecological crises of the 20th century. Even with large reductions in phosphorus loading, termed "reoligotrophication," DOB and chlorophyll (CHL) have often not returned to their expected pre-20th-century levels. Concurrently, management of lake health has been confounded by possible consequences of climate change, particularly since the effects of climate are not neatly separable from the effects of eutrophication. Here, using Lake Geneva as an iconic example, we demonstrate a complementary alternative to parametric models for understanding and managing lake systems. This involves establishing an empirically-driven baseline that uses supervised machine learning to capture the changing interdependencies among biogeochemical variables and then combining the empirical model with a more conventional equation-based model of lake physics to predict DOB over decadal time-scales. The hybrid model not only leads to substantially better forecasts, but also to a more actionable description of the emergent rates and processes (biogeochemical, ecological, etc.) that drive water quality. Notably, the hybrid model suggests that the impact of a moderate 3°C air temperature increase on water quality would be on the same order as the eutrophication of the previous century. The study provides a template and a practical path forward to cope with shifts in ecology to manage environmental systems for non-analogue futures.

Environmental context dependency in species interactions.

Ecological interactions are not uniform across time and can vary with environmental conditions. Yet, interactions among species are often measured with short-term controlled experiments whose outcomes can depend greatly on the particular environmental conditions under which they are performed. As an alternative, we use empirical dynamic modeling to estimate species interactions across a wide range of environmental conditions directly from existing long-term monitoring data. In our case study from a southern California kelp forest, we test whether interactions between multiple kelp and sea urchin species can be reliably reconstructed from time-series data and whether those interactions vary predictably in strength and direction across observed fluctuations in temperature, disturbance, and low-frequency oceanographic regimes. We show that environmental context greatly alters the strength and direction of species interactions. In particular, the state of the North Pacific Gyre Oscillation seems to drive the competitive balance between kelp species, asserting bottom-up control on kelp ecosystem dynamics. We show the importance of specifically studying variation in interaction strength, rather than mean interaction outcomes, when trying to understand the dynamics of complex ecosystems. The significant context dependency in species interactions found in this study argues for a greater utilization of long-term data and empirical dynamic modeling in studies of the dynamics of other ecosystems.

Cost-effectiveness of 4CMenB vaccination against gonorrhea: importance of dosing schedule, vaccine sentiment, targeting strategy, and duration of protection.

BACKGROUND: Observational evidence suggests the 4CMenB meningococcal vaccine may partially protect against gonorrhea, with one dose being two-thirds as protective as two. We examined the cost-effectiveness of vaccinating men-who-have-sex-with-men (MSM) in England, with one- or two-dose primary vaccination. METHODS: Integrated transmission-dynamic health-economic modeling explored the effects of targeting strategy, first- and second-dose uptake levels, and duration of vaccine protection, using observational estimates of vaccine protection. RESULTS: Vaccination with one or two primary doses is always cost-saving, irrespective of uptake, although vaccine sentiment is an important determinant of impact and cost-effectiveness. The most impactful and cost-effective targeting is offering "Vaccination-according-to-Risk" (VaR), to all patients with gonorrhea plus those reporting high numbers of sexual partners. If VaR is not feasible to implement then the more-restrictive strategy of "Vaccination-on-Diagnosis" (VoD) with gonorrhea is cost-effective, but much less impactful. Under conservative assumptions, VaR(2-dose) saves £7.62M(95%CrI:1.15-17.52) and gains 81.41(28.67-164.23) QALYs over 10 years; VoD(2-dose) saves £3.40M(0.48-7.71) and gains 41.26(17.52-78.25) QALYs versus no vaccination. Optimistic versus pessimistic vaccine-sentiment assumptions increase net benefits by ∼30%(VoD) or ∼60%(VaR). CONCLUSIONS: At UK costs, targeted 4CMenB vaccination of MSM gains QALYs and is cost-saving at any uptake level. Promoting uptake maximizes benefits and is an important role for behavioral science.

Determining Herd Immunity Thresholds for Hepatitis A Virus Transmission to Inform Vaccination Strategies Among People Who Inject Drugs in 16†US States.

BACKGROUND: Widespread outbreaks of person-to-person transmitted hepatitis A virus (HAV), particularly among people who inject drugs (PWID), continue across the United States and globally. However, the herd immunity threshold and vaccination coverage required to prevent outbreaks are unknown. We used surveillance data and dynamic modeling to estimate herd immunity thresholds among PWID in 16 US states. METHODS: We used a previously published dynamic model of HAV transmission calibrated to surveillance data from outbreaks involving PWID in 16 states. Using state-level calibrated models, we estimated the basic reproduction number (R0) and herd immunity threshold for PWID in each state. We performed a meta-analysis of herd immunity thresholds to determine the critical vaccination coverage required to prevent most HAV outbreaks among PWID. RESULTS: Estimates of R0 for HAV infection ranged from 2.2 (95% confidence interval [CI], 1.9-2.5) for North Carolina to 5.0 (95% CI, 4.5-5.6) for West Virginia. Corresponding herd immunity thresholds ranged from 55% (95% CI, 47%-61%) for North Carolina to 80% (95% CI, 78%-82%) for West Virginia. Based on the meta-analysis, we estimated a pooled herd immunity threshold of 64% (95% CI, 61%-68%; 90% prediction interval, 52%-76%) among PWID. Using the prediction interval upper bound (76%) and assuming 95% vaccine efficacy, we estimated that vaccination coverage of 80% could prevent most HAV outbreaks. CONCLUSIONS: Hepatitis A vaccination programs in the United States may need to achieve vaccination coverage of at least 80% among PWID in order to prevent most HAV outbreaks among this population.

Simultaneous multi-transient linear-combination modeling of MRS data improves uncertainty estimation.

PURPOSE: The interest in applying and modeling dynamic MRS has recently grown. Two-dimensional modeling yields advantages for the precision of metabolite estimation in interrelated MRS data. However, it is unknown whether including all transients simultaneously in a 2D model without averaging (presuming a stable signal) performs similarly to one-dimensional (1D) modeling of the averaged spectrum. Therefore, we systematically investigated the accuracy, precision, and uncertainty estimation of both described model approaches. METHODS: Monte Carlo simulations of synthetic MRS data were used to compare the accuracy and uncertainty estimation of simultaneous 2D multitransient linear-combination modeling (LCM) with 1D-LCM of the average. A total of 2,500 data sets per condition with different noise representations of a 64-transient MRS experiment at six signal-to-noise levels for two separate spin systems (scyllo-inositol and gamma-aminobutyric acid) were analyzed. Additional data sets with different levels of noise correlation were also analyzed. Modeling accuracy was assessed by determining the relative bias of the estimated amplitudes against the ground truth, and modeling precision was determined by SDs and Cramér-Rao lower bounds (CRLBs). RESULTS: Amplitude estimates for 1D- and 2D-LCM agreed well and showed a similar level of bias compared with the ground truth. Estimated CRLBs agreed well between both models and with ground-truth CRLBs. For correlated noise, the estimated CRLBs increased with the correlation strength for the 1D-LCM but remained stable for the 2D-LCM. CONCLUSION: Our results indicate that the model performance of 2D multitransient LCM is similar to averaged 1D-LCM. This validation on a simplified scenario serves as a necessary basis for further applications of 2D modeling.

Toward a dynamic immunopsychiatry.

In this article, we briefly clarify several points regarding immunopsychiatry. In particular, we argue that higher density data and a greater focus on temporal dynamics are both important, and that studies incorporating these features have the potential to greatly advance the field. We also respond to recent comments made on our original article on this topic (Moriarity and Slavich, 2023), including the contention that our perspective on immunopsychiatry is reductionistic. To the contrary, we believe that strong immunopsychiatry studies are highly integrative and include data from multiple major levels of analysis to form a more complete picture of how processes that are relevant for mental health and behavior emerge and dynamically change over time in relation to one another.

Modeling Microbial Community Networks: Methods and Tools for Studying Microbial Interactions.

Microbial interactions function as a fundamental unit in complex ecosystems. By characterizing the type of interaction (positive, negative, neutral) occurring in these dynamic systems, one can begin to unravel the role played by the microbial species. Towards this, various methods have been developed to decipher the function of the microbial communities. The current review focuses on the various qualitative and quantitative methods that currently exist to study microbial interactions. Qualitative methods such as co-culturing experiments are visualized using microscopy-based techniques and are combined with data obtained from multi-omics technologies (metagenomics, metabolomics, metatranscriptomics). Quantitative methods include the construction of networks and network inference, computational models, and development of synthetic microbial consortia. These methods provide a valuable clue on various roles played by interacting partners, as well as possible solutions to overcome pathogenic microbes that can cause life-threatening infections in susceptible hosts. Studying the microbial interactions will further our understanding of complex less-studied ecosystems and enable design of effective frameworks for treatment of infectious diseases.

Worst-case scenarios: Modeling uncontrolled type 2 polio transmission.

In May 2016, the Global Polio Eradication Initiative (GPEI) coordinated the cessation of all use of type 2 oral poliovirus vaccine (OPV2), except for emergency outbreak response. Since then, paralytic polio cases caused by type 2 vaccine-derived polioviruses now exceed 3,000 cases reported by 39 countries. In 2022 (as of April 25, 2023), 20 countries reported detection of cases and nine other countries reported environmental surveillance detection, but no reported cases. Recent development of a genetically modified novel type 2 OPV (nOPV2) may help curb the generation of neurovirulent vaccine-derived strains; its use since 2021 under Emergency Use Listing is limited to outbreak response activities. Prior modeling studies showed that the expected trajectory for global type 2 viruses does not appear headed toward eradication, even with the best possible properties of nOPV2 assuming current outbreak response performance. Continued persistence of type 2 poliovirus transmission exposes the world to the risks of potentially high-consequence events such as the importation of virus into high-transmission areas of India or Bangladesh. Building on prior polio endgame modeling and assuming current national and GPEI outbreak response performance, we show no probability of successfully eradicating type 2 polioviruses in the near term regardless of vaccine choice. We also demonstrate the possible worst-case scenarios could result in rapid expansion of paralytic cases and preclude the goal of permanently ending all cases of poliomyelitis in the foreseeable future. Avoiding such catastrophic scenarios will depend on the development of strategies that raise population immunity to type 2 polioviruses.

Coordinated global cessation of oral poliovirus vaccine use: Options and potential consequences.

Due to the very low, but nonzero, paralysis risks associated with the use of oral poliovirus vaccine (OPV), eradicating poliomyelitis requires ending all OPV use globally. The Global Polio Eradication Initiative (GPEI) coordinated cessation of Sabin type 2 OPV (OPV2 cessation) in 2016, except for emergency outbreak response. However, as of early 2023, plans for cessation of bivalent OPV (bOPV, containing types 1 and 3 OPV) remain undefined, and OPV2 use for outbreak response continues due to ongoing transmission of type 2 polioviruses and reported type 2 cases. Recent development and use of a genetically stabilized novel type 2 OPV (nOPV2) leads to additional potential vaccine options and increasing complexity in strategies for the polio endgame. Prior applications of integrated global risk, economic, and poliovirus transmission modeling consistent with GPEI strategic plans that preceded OPV2 cessation explored OPV cessation dynamics and the evaluation of options to support globally coordinated risk management efforts. The 2022-2026 GPEI strategic plan highlighted the need for early bOPV cessation planning. We review the published modeling and explore bOPV cessation immunization options as of 2022, assuming that the GPEI partners will not support restart of the use of any OPV type in routine immunization after a globally coordinated cessation of such use. We model the potential consequences of globally coordinating bOPV cessation in 2027, as anticipated in the 2022-2026 GPEI strategic plan. We do not find any options for bOPV cessation likely to succeed without a strategy of bOPV intensification to increase population immunity prior to cessation.

Compound Fault Characteristic Analysis for Fault Diagnosis of a Planetary Gear Train.

The carrier eccentricity error and gear compound faults are most likely to occur simultaneously in an actual planetary gear train (PGT). Various faults and errors are coupled with each other to generate a complex dynamic response, which makes the diagnosis of PGT faults difficult in practice. In order to analyze the joint effect of the error and the compound faults in a PGT, a carrier eccentricity error model is proposed and incorporated into the TVMS model by considering the time-varying center distance, line of action (LOA), meshing angle, and contact ratio. Then, the TVMS of the cracked gear is derived based on the potential energy method. On this basis, the dynamic model of a PGT with both the carrier eccentricity error and compound gear cracks as internal excitations are established. Furthermore, the meshing characteristics and dynamic responses of the PGT are simulated to investigate the compound fault features. A series of experiments are conducted to further analyze the influence of the compound fault on the vibration response. The relevant conclusions can provide a reference for the compound fault diagnosis of a PGT in practice.

Bearing Dynamics Modeling Based on the Virtual State-Space and Hammerstein-Wiener Model.

This study investigates a novel approach for assessing the health status of rotating machinery transmission systems by analyzing the dynamic degradation of bearings. The proposed method generates multi-dimensional data by creating virtual states and constructs a multi-dimensional model using virtual state-space in conjunction with mechanism model analysis. Innovatively, the Hammerstein-Wiener (HW) modeling technique from control theory is applied to identify these dynamic multi-dimensional models. The modeling experiments are performed, focusing on the model's input and output types, the selection of nonlinear module estimators, the configuration of linear module transfer functions, and condition transfer. Dynamic degradation response signals are generated, and the method is validated using four widely recognized databases consisting of accurate measurement signals collected by vibration sensors. Experimental results demonstrated that the model achieved a modeling accuracy of 99% for multiple bearings under various conditions. The effectiveness of this dynamic modeling method is further confirmed through comparative experimental data and signal images. This approach offers a novel reference for evaluating the health status of transmission systems.

The future is dynamic: A call for intensive longitudinal data in immunopsychiatry.

The long-term value of immunopsychiatry will be based on its ability to translate basic science into effective clinical interventions. In this article, we discuss a key obstacle to achieving this important translational goal-namely, the preponderance of studies that are cross-sectional, or that have months-to-years long follow-up periods. Immunopsychiatric processes such as stress, inflammation, and depression symptoms are inherently dynamic and fluctuate over hours, days, and weeks. This fact suggests that higher-density data collection with only days between measurements is necessary to capture-with adequate resolution-the actual dynamics of these systems, determine optimal time lags with which to observe associations between variables of interest, and maximize the translational potential of these data. To illustrate these points, we use pilot data from our own intensive longitudinal immunopsychiatric study. We then conclude by making several recommendations for future research. By learning how to better use existing data for dynamically informative studies as well as collecting intensive longitudinal data, we believe immunopsychiatry will be much better positioned to advance our causal understanding of the interplay between the immune system and health.

Dynamics and energetics of bottlenose dolphin (Tursiops truncatus) fluke-and-glide gait.

Intermittent locomotion composed of periods of active flapping/stroking followed by inactive gliding has been observed with species that inhabit both aerial and marine environments. However, studies on the energetic benefits of a fluke-and-glide (FG) gait during horizontal locomotion are limited for dolphins. This work presents a physics-based model of FG gait and an analysis of the associated cost of transport for bottlenose dolphins (Tursiops truncatus). New gliding drag coefficients for the model were estimated using measured data from free-swimming bottlenose dolphins. The data-driven approach used kinematic measurement from 84 h of biologging tag data collected from three animals to estimate the coefficients. A set of 532 qualifying gliding events were automatically extracted for estimation of the gliding drag coefficient. Next, data from 783 FG bouts were parameterized and used with the model-based dynamic analysis to investigate the cost benefits of FG gait. Experimental results indicate that FG gait was preferred at speeds of ∼2.2-2.7 m s-1. Observed FG bouts had an average duty factor of 0.45 and a gliding duration of 5 s. The average associated metabolic cost of transport (COT) and mechanical cost of transport (MECOT) of FG gait are 2.53 and 0.35 J m-1 kg-1, respectively, at the preferred speeds. This corresponded to a respective 18.9% and 27.1% reduction in cost when compared with model predictions of continuous fluking gait at the same average bout speed. Average thrust was positively correlated with fluking frequency and amplitude as animals accelerated during the FG bouts, whereas fluking frequency and amplitude were negatively correlated for a given thrust range. These results suggest that FG gait enhances the horizontal swimming efficiency of bottlenose dolphins and provides new insights into the gait dynamics of these animals.

Population impact of fine particulate matter on tuberculosis risk in China: a causal inference.

BACKGROUND: Previous studies have suggested the potential association between air pollution and tuberculosis incidence, but this association remains inconclusive and evidence to assess causality is particularly lacking. We aimed to draw causal inference between fine particulate matter less than 2.5 µm in diameter (PM2.5) and tuberculosis in China. METHODS: Granger causality (GC) inference was performed within vector autoregressive models at levels and/or first-differences using annual national aggregated data during 1982-2019, annual provincial aggregated data during 1982-2019 and monthly provincial aggregated data during 2004-2018. Convergent cross-mapping (CCM) approach was used to determine the backbone nonlinear causal association based on the monthly provincial aggregated data during 2004-2018. Moreover, distributed lag nonlinear model (DLNM) was applied to quantify the causal effects. RESULTS: GC tests identified PM2.5 driving tuberculosis dynamics at national and provincial levels in Granger sense. Empirical dynamic modeling provided the CCM causal intensity of PM2.5 effect on tuberculosis at provincial level and demonstrated that PM2.5 had a positive effect on tuberculosis incidence. Then, DLNM estimation demonstrated that the PM2.5 exposure driven tuberculosis risk was concentration- and time-dependent in a nonlinear manner. This result still held in the multi-pollutant model. CONCLUSIONS: Causal inference showed that PM2.5 exposure driving tuberculosis, which showing a concentration gradient change. Air pollutant control may have potential public health benefit of decreasing tuberculosis burden.

Modeling scenarios for ending poliovirus transmission in Pakistan and Afghanistan.

Pakistan and Afghanistan pose risks for international transmission of polioviruses as the last global reservoir for wild poliovirus type 1 (WPV1) and a reservoir for type 2 circulating vaccine-derived polioviruses (cVDPV2s). Widespread transmission of WPV1 and cVDPV2 in 2019-2020 and resumption of intensive supplemental immunization activities (SIAs) in 2020-2021 using oral poliovirus vaccine (OPV) led to decreased transmission of WPV1 and cVDPV2 as of the end of 2021. Using an established dynamic disease transmission model, we explore multiple bounding scenarios with varying intensities of SIAs using bivalent OPV (bOPV) and/or trivalent tOPV (tOPV) to characterize potential die out of transmission. This analysis demonstrates potential sets of actions that may lead to elimination of poliovirus transmission in Pakistan and/or Afghanistan. Some modeled scenarios suggest that Pakistan and Afghanistan could increase population immunity to levels high enough to eliminate transmission, and if maintained, achieve WPV1 and cVDPV2 elimination as early as 2022. This requires intensive and proactive OPV SIAs to prevent transmission, instead of surveillance followed by reactive outbreak response. The reduction of cases observed in 2021 may lead to a false sense of security that polio has already or soon will die out on its own, but relaxation of immunization activities runs the risk of lowering population immunity to, or below, the minimum die-out threshold such that transmission continues. Transmission modeling may play a key role in managing expectations and supporting future modeling about the confidence of no virus circulation in anticipation of global certification decisions.

Fitting Bayesian Stochastic Differential Equation Models with Mixed Effects through a Filtering Approach.

Recent advances in technology contribute to a fast-growing number of studies utilizing intensive longitudinal data, and call for more flexible methods to address the demands that come with them. One issue that arises from collecting longitudinal data from multiple units in time is nested data, where the variability observed in such data is a mixture of within-unit changes and between-unit differences. This article aims to provide a model-fitting approach that simultaneously models the within-unit changes with differential equation models and accounts for between-unit differences with mixed effects. This approach combines a variant of the Kalman filter, the continuous-discrete extended Kalman filter (CDEKF), and the Markov chain Monte Carlo method often employed in the Bayesian framework through the platform Stan. At the same time, it utilizes Stan's functionality of numerical solvers for the implementation of CDEKF. For an empirical illustration, we applied this method in the context of differential equation models to an empirical dataset to explore the physiological dynamics and co-regulation between couples.

A Hybrid Controller for a Soft Pneumatic Manipulator Based on Model Predictive Control and Iterative Learning Control.

Due to the outstanding characteristics of the large structural flexibility and strong dexterity of soft robots, they have attracted great attention. However, the dynamic modeling and precise control of soft robots face huge challenges. Traditional model-based and model-free control methods find it difficult to obtain a balance between complexity and accuracy. In this paper, a dynamic model of a three-chamber continuous pneumatic manipulator is established based on the modal method. Moreover, a hybrid controller integrating model predictive control (MPC) and iterative learning control (ILC) is proposed, which can simultaneously perform model parameter learning and trajectory tracking control. Experimental results show that the proposed control method can optimize the parameters of the dynamic model in real time with less iterations than the traditional model-free method and have good control performance in trajectory tracking experiments. In the future, the proposed dynamic model and the hybrid controller should be verified on a multi-section manipulator.