Temporal Fluctuations Research Articles

The Navier-Stokes equation and a fully developed turbulence

In fairly general conditions we give explicit (smooth) solutions for the potential flow. We show that, rigorously speaking, the equations of the fluid mechanics have not rotational solutions. However, within the usual approximations of an incompressible fluid and an isentropic flow, the Navier-Stokes equation has approximate vorticial (rotational) solutions, generated by viscosity. However, in general, these vortices are unstable, and a discrete distribution of vorticial solutions is not in me chanical equilibrium; it forms an unstable vorticial liquid. On the other hand, these solutions may exhibit turbulent, fluctuating instabilities for large variations of the velocity over short distances. We represent a fully developed turbulence as a homogeneous, isotropic and highly-fluctuating distribution of singular centres of turbulence. A regular mean flow can be included. In these circumstances the Navier-Stokes equation exhibits three time scales. The equations of the mean flow can be disentangled from the equations of the fluctuating part, which is reduced to a vanishing inertial term. This latter equation is not satisfied after averaging out the temporal fluctuations. For a homogeneous and isotropic distribution of non-singular turbulence centres the equation for the inertial term is satisfied trivially, i.e. both the average fluctuating velocity and the average fluctuating inertial term are zero. If the velocity is singular at the turbulence centres, we are left with a quasi-ideal classical gas of singularities, or a solution of singularities (solute) in quasi thermal equilibrium in the background fluid (solvent). This is an example of an emergent dynamics. We give three examples of vorticial liquids.

1/f noise in semiconductors arising from the heterogeneous detrapping process of individual charge carriers

Abstract We propose a model of 1 / f noise in semiconductors based on the drift of individual charge carriers and their interaction with the trapping centers. We assume that the trapping centers are homogeneously distributed in the material. The trapping centers are assumed to be heterogeneous and have unique detrapping rates. We show that uniform detrapping rate distribution emerges as a natural consequence of the vacant trap depths following the Boltzmann distribution, and the detrapping process obeying Arrhenius law. When these laws apply, and if the trapping rate is low in comparison to the maximum detrapping rate, 1 / f noise in the form of Hooge’s relation is recovered. Hooge’s parameter, α H, is shown to be a ratio between the characteristic trapping rate and the maximum detrapping rate. The proposed model implies that 1 / f noise arises from the temporal charge carrier number fluctuations, not from the spatial mobility fluctuations.

Magnetic resonance-conditional cardiac implantable electronic devices: an Italian perspective on the prevalence of mixed-brand systems over time

The historical restriction of magnetic resonance imaging (MRI) for patients with cardiac implantable electronic devices (CIEDs) has been lifted by certified MRI-conditional systems in recent years. Mixed-brand CIED systems consisting of a generator from one manufacturer and at least one lead from another manufacturer are not certified for MRI. We evaluated the temporal trend in the prevalence of mixed-brand systems in the era of MRI-conditional systems. Data were analyzed on 5853 CIEDs implanted de novo between 2012 and 2022 in 81 Italian centers linked to the nationwide Home Monitoring Expert Alliance network. The percentage of mixed-brand implants was calculated by device type (pacemaker, implantable cardioverter-defibrillator [ICD], cardiac resynchronization therapy [CRT] device) and over time. A mixed-brand system was implanted in 4.1% (95% CI, 3.6-4.6%) of analyzed patients or, by device type, in 4.5% (3.5-5.7%) of pacemaker patients, 1.1% (0.7-1.7%) of ICD patients, and 6.8% (5.7-7.9%) of CRT pacemaker/defibrillator patients (p < 0.001). Prevalence of mixed-brand implants exhibited significant temporal fluctuations, first declining from 6.6% (2012–2014) to 1.3% (2019), and then increasing to 5.1% (2022). Temporal changes were statistically significant for pacemakers (p < 0.001) and CRT devices (p = 0.001), but not for ICDs (p = 0.438). In the decade between 2012 and 2022, mixed-brand CIED systems were more prevalent in patients treated with pacemakers and CRT devices than in ICD recipients. A decline in the prevalence of mixed-brand systems was observed after the introduction of MRI-conditional systems, reaching a minimum in 2019, followed by a progressive increase in the subsequent years.

A Rapid and Simplified Approach to Correct Atmospheric Absorptions in Infrared Spectra.

Infrared (IR) spectroscopy is a powerful analytical technique used to identify and quantify different components within a sample. However, spectral interference from fluctuating concentrations of water vapor and CO2 in the measurement chamber can significantly impede the extraction of quantitative information. These temporal fluctuations cause absorption variations that interfere with the sample's spectrum, making accurate analysis challenging. While several techniques to overcome this problem exist in the literature, many are time-consuming or ineffective. We present a simple method utilizing just two sample spectra taken sequentially. The difference of these spectra, multiplied by a scaling factor, determined by minimization of the point-to-point spectral length, provides a correction spectrum. Subtracting this from the spectrum to be corrected results in a fully corrected spectrum. We demonstrate the effectiveness of this method via the improved ability to determine analyte concentration from corrected spectra over uncorrected spectra using a partial least square regression (PLSR) model. This technique therefore offers rapid, effective, and automated spectral correction, which is ideal for a nonexpert user in a clinical or industrial setting.

Modulation Interference from Speech-Modulated Noise in Bimodal Cochlear Implant Users

Purpose: This study examined modulation interference in sentence recognition for bimodal cochlear implant (CI) users.Methods: Thirteen bimodal users and thirteen normal-hearing (NH) listeners (reference) participated in this study. The adaptive Korean Matrix sentence-in-noise test measured the speech reception thresholds (SRTs) required for 50% speech-in-noise intelligibility. As background noise, we presented two background noises: stationary speech-shaped noise (SSN) and single-talker speech-modulated masker (International Collegium of Rehabilitative Audiology noise, ICRA). The amount of masking release due to fluctuations in the speech-modulated noise, called speech masking release (SMR), was calculated from the difference between SRTs with SSN and SRTs with ICRA noise. The SRTs were measured twice for bimodal CI users: CI-only and bimodal listening.Results: The mean SMR for NH listeners was approximately 12 dB. The mean SMR for bimodal CI users was about -5 dB, with either CI alone or bimodal listening. This means that bimodal CI users experienced modulation interference rather than masking release from the temporal fluctuations. We observed positive bimodal benefits, yet the interference from fluctuating maskers was similar between the CI-only and bimodal listening. The low-frequency aided hearing thresholds in the non-implanted ear were unrelated to the amount of SMR for bimodal users.Conclusion: NH listeners could take advantage of temporal fluctuations in the speech-modulated noise. However, bimodal CI listeners hardly glimpsed target speech in the dips of the speech-modulated masker, regardless of listening mode. Bimodal listening did not significantly reduce modulation interference compared to CI-only listening.

Daily estimation of NO2 concentrations using digital tachograph data.

Traffic information is crucial for estimating NO2 concentrations, but it is static and limited in predicting constantly changing NO2 levels. To overcome these challenges, this study utilized real-time spatial big data to capture both the spatial and temporal fluctuations in traffic. Digital tachograph (DTG) data, sourced from digital devices in all commercial vehicles, are employed to construct a DTG land use regression (LUR) model, and its performance is compared with that of a non-DTG-LUR model. The DTG-LUR model exhibits superior performance, with an explanatory power of 0.46, in contrast to the 0.36 of the non-DTG model. This significant improvement stems from the spatially and temporally dynamic DTG variables such as cargo traffic. This study introduces a novel approach for incorporating DTG data in correlating with NO2 concentrations. It underscores the advantage of DTG data in predicting daily NO2 fluctuations at a precise 200-m grid, which is not feasible with conventional data. The findings of the study highlight the immense potential of spatial big data for fine-grained analyses, which could enable hourly predictions of air pollution.

Adaptive-modulated fast fluctuation super-resolution microscopy

Fluorescence microscopy has significantly advanced biological imaging at the nanoscale, particularly with the advent of super-resolution microscopy (SRM), which transcends the Abbe diffraction limit. Most cutting-edge SR methods require high-precision optical setups, which constrain the widespread adoption of SRM. Fluorescence fluctuation-based SRM (FF-SRM) can break the diffraction limit without complex optical components, making it particularly well-suited for biological imaging. However, conventional FF-SRM methods, such as super-resolution optical fluctuation imaging (SOFI), still require specific fluorescent molecular blinking properties. Instead of enhancing the intrinsic blinking characteristics by finding specific fluorescent markers, employing optical methods such as spatial light modulation to adjust the excitation light field allows for easier and more flexible matching of the on-time ratio with the analysis of temporal stochastic intensity fluctuations. Nevertheless, the specific parameters of the modulation patterns have not been thoroughly explored, despite their crucial influence on the reconstruction quality. Herein, we propose adaptive-modulated fast fluctuation super-resolution microscopy. Our method demonstrates theoretically and experimentally that restricting the size of modulation units in a certain range ensures better image quality with fewer artifacts and signal losses. We find it still significantly effective when applied to other FF-SRM. Overall, the further development of the adaptive modulation technique has made it more stable in behavior and maintained high-quality imaging, presenting broader prospects for super resolution imaging based on statistical analysis.

Autism Identification Based on the Intelligent Analysis of Facial Behaviors: An Approach Combining Coarse- and Fine-Grained Analysis

Background: Facial behavior has emerged as a crucial biomarker for autism identification. However, heterogeneity among individuals with autism poses a significant obstacle to traditional feature extraction methods, which often lack the necessary discriminative power. While deep-learning methods hold promise, they are often criticized for their lack of interpretability. Methods: To address these challenges, we developed an innovative facial behavior characterization model that integrates coarse- and fine-grained analyses for intelligent autism identification. The coarse-grained analysis provides a holistic view by computing statistical measures related to facial behavior characteristics. In contrast, the fine-grained component uncovers subtle temporal fluctuations by employing a long short-term memory (LSTM) model to capture the temporal dynamics of head pose, facial expression intensity, and expression types. To fully harness the strengths of both analyses, we implemented a feature-level attention mechanism. This not only enhances the model’s interpretability but also provides valuable insights by highlighting the most influential features through attention weights. Results: Upon evaluation using three-fold cross-validation on a self-constructed autism dataset, our integrated approach achieved an average recognition accuracy of 88.74%, surpassing the standalone coarse-grained analysis by 8.49%. Conclusions: This experimental result underscores the improved generalizability of facial behavior features and effectively mitigates the complexities stemming from the pronounced intragroup variability of those with autism, thereby contributing to more accurate and interpretable autism identification.

Discreet but diverse and specific: Determining plant‐herbivore interactions across a species‐rich plant family in a tropical rain forest

AbstractStudying plant–herbivore interactions within tropical rain forests is fundamental to understanding their ecology and evolution. An important aspect of plant–herbivore dynamics is the role of temporal and taxonomic variables in determining associations between herbivores and their host. Using the diverse and chemically rich plant family Annonaceae (Magnoliales), we conducted a year‐long study in Ecuador's Yasuní National Park in lowland Amazonia. We focused on nine understory tree species across a broad phylogenetic range within Annonaceae. For these species, we investigated patterns of herbivory, identified herbivores through DNA barcoding, and documented unique ant–butterfly associations. In general, leaf damage ranged from 0.09% to 25%, with significant temporal fluctuations for three species. Notably, Anaxagorea brevipes and Unonopsis veneficiorum faced higher herbivore pressure when compared to the other studied species. We document a discreet but diverse herbivore community, with 40 larvae from 12 Lepidoptera families collected throughout the year. Our findings identify, for the first time across a phylogenetically diverse sampling of Annonaceae, the specialization of herbivores on our focal species. Overall, our data provide valuable information on herbivory patterns at the local scale for this important rain forest plant family. Furthermore, these findings contribute to our understanding of the ecological processes that influence plant species diversity in tropical rain forests.Abstract in Spanish is available with online material.

Study on Evaluation and Prediction Model of Long-term Mechanical Properties of Fine-grained Saline Soil Subgrade

In this thorough examination, we dive deep into the long-lasting mechanical characteristics of fine-grained saline soil subgrades, aspiring to establish a precise and reliable collection of predictive models. Our objective is to provide a solid scientific footing for the design and ongoing upkeep of road networks within saline soil environments. Analyzing prolonged monitoring data across diverse highway subgrades within a prototypical saline soil locale, we unveil the intricate temporal fluctuations and environmental sensitivities of the soil’s mechanical properties under continuous load. Precisely, the subgrade’s compressive modulus dwindled by 15%, while shear strength declined by 8% over a five-year period. These trends intensify during rainy and scorching seasons, with drops surpassing 20% and 12% respectively. Leveraging this intricate data, we deploy nonlinear regression analysis and sophisticated machine learning algorithms to construct a predictive model tailored for the long-term mechanical properties of fine-grained saline soil roadbeds. This model integrates a multitude of factors, including load duration, temperature, humidity, and more, delivering accurate forecasts of key subgrade indicators like compressive modulus, shear strength, and beyond. In the verification stage, compared with the measured data, the error rate of the model prediction results is controlled within 5%, showing high prediction accuracy and stability. In addition, we also carried on the sensitivity analysis to the model, found that the load size and the duration of the impact on the mechanical properties of the roadbed is the most significant. Therefore, in the design of road engineering in saline soil areas, the influence of these factors should be fully considered, and reasonable engineering measures should be taken to ensure the safety and durability of roads. This study not only provides effective data support for the long-term mechanical performance evaluation of fine-grained saline soil roadbed, but also provides an important theoretical reference for engineering practice in related fields.

Reconstructing the Ocean State Using Argo Data and a Data-Driven Method

Abstract Over the past two decades, Argo profiles have provided unprecedented insight into the global patterns of space and time variability of ocean temperature and salinity, significantly reducing associated uncertainties. However, analyzing such assessments during the pre-Argo period remains challenging due to the scarcity of observations in many regions. From the Argo period, a set of dominant three-dimensional patterns can be estimated using Empirical Orthogonal Function (EOF) analysis, which helps to fill in observational gaps. From the associated principal components, temporal fluctuations can be observed, aiming to build a catalog of possible ocean state trajectories. To map pre-Argo observations, EOFs are used in a data assimilation framework that uses this catalog to feed an analog prediction and provide reanalysis. In this study, a new data-driven interpolation method called RedAnDA (Reduced-space Analog Data Assimilation) was tested in the tropical Pacific Ocean. RedAnDA was first validated through an Observing System Simulation Experiment (OSSE) approach, using synthetic observations extracted from a model simulation. It was subsequently applied to a real historical dataset and compared with other available reanalysis products. Overall, the reconstructed temperature field showed variability consistent with the OSSE true field and other reanalysis products in the real data application. Further improvements are needed to optimally estimate uncertainty, but RedAnDA already combines valuable information about state predictability, observational sampling, and unresolved scale issues.

The only constant is change: Stable vs. variable aspects of food approach bias relate differently to food craving and intake

The tendency to approach food faster than to avoid it (i.e., approach bias) is thought to facilitate food intake, particularly foods that conflict with one's dietary goals. However, this relationship has been difficult to demonstrate, which ties into an ongoing debate about whether such cognitive-behavioral biases represent stable traits or fluctuating states. We thus investigated the temporal fluctuations of food approach bias (1), its within-participant association with food craving (2) and intake (3), and the role of top-down control in this bias-intake association (4). The 76 participants completed an impulsivity questionnaire and performed a smartphone-based approach-avoidance task on nine days. Every day, they also reported their daily craving, intake, and dietary intentions for 12 personalized foods they wanted to eat less or more often over the study period. Approach bias varied considerably within individuals (1), and correlated in unexpected ways with food craving (2) and intake (3); this association of approach bias with intake was moderated by inter-individual differences (rather than day-to-day fluctuations) in dietary intentions and impulsivity (4). Results emphasize the need to re-conceptualize approach bias as comprising both state and trait components, and indicate that the more trait-like aspects of top-down control gate the relationship of approach bias with intake. The large day-to-day variation in approach bias may explain why single-session bias measures often do not predict distal outcomes like body weight. Furthermore, our results suggest that interventions targeting approach bias may be most effective for certain timepoints (high-risk situations) and individuals (those with weak dietary intentions).

Assessment of indoor radon levels at multiple floors of an apartment building in the historic center of Rome (Italy): a comprehensive study

The urbanized area of Rome is largely built over volcanic deposits, characterized by a significant radionuclides content and consequently a high radon emanation potential. An accurate monitoring of workplaces and residential dwellings constitutes a first step towards mitigating the indoor radon exposure. Since radon diffusion dynamics involves complex interactions among many environmental parameters on different time scales, a proper assessment of radon concentration variations can be better achieved by means of active monitoring approaches. We present here the results of 1 year of continuous measurements conducted in six premises (five apartments and a basement) at different floors of the same building in the Esquilino district, in the historical center of Rome. Collecting annual series of radon concentration enables us to identify fluctuations over a seasonal scale, with radon generally decreasing in the warm season. The simultaneous tracking of different floors should cancel the influence of geogenic radon and of building characteristics like age, typology, and construction materials. While the basement shows the highest radon concentration, indicating a major contribution from the ground, we observe indoor radon levels comparable at all the upper floors, questioning the common belief that high-risk exposure be limited to the lowest storeys. The use of active devices makes it possible to discriminate between average indoor radon measured during the day and overnight, when residents are more likely to be at home. Our analysis provides the characterization of the dynamics of the gas emanation and transport inside the buildings and of its temporal fluctuations, in relation to the environmental and meteorological conditions. Since the experiment was performed in the Roman urban contest, we cannot ignore the specificity of the retrieved data, affected not only by endogenous factors like life habits relative to ventilation and conditioning of the apartments, but also by exogenous factors, among which the warmer microclimate compared to the surrounding suburban and rural areas, due to the effects of urbanization (urban heat island effect).

Skin Bacterial and Fungal Microbiome Responses to Diet Supplementation and Rewilding in the Critically Endangered Southern Corroboree Frog.

The composition and dynamics of the skin bacterial and fungal microbiome is thought to influence host-pathogen defence. This microbial community is shaped by host captivity, diet, and microbial interactions between bacterial and fungal components. However, there remains little understanding of how specific micronutrients influence bacterial and fungal microbiome composition and their inter-domain interactions during rewilding of captive-bred animals. This study experimentally investigated the effect of dietary beta-carotene supplementation and subsequent field release on bacterial and fungal microbiome composition and dynamics using the Southern Corroboree frog (Pseudophryne corroboree) as a model system. We found large-scale diversification of bacterial communities post-release and similar diversification of fungal communities. The rewilded fungal mycobiome was more transient and demonstrated stronger temporal and micro-spatial fluctuations than the bacterial microbiome. Accounting for temporal and spatial factors, we found strong residual associations between bacterial members, yet limited evidence for inter-domain associations, suggesting that co-occurrence patterns between bacterial and fungal communities are largely a result of shared responses to the environment rather than direct interactions. Lastly, we found supplementation of dietary beta-carotene in captivity had no impact on post-release microbiome diversity, yet was associated with approximately 15% of common bacterial and fungal genera. Our research demonstrates that environmental factors play a dominant role over dietary beta-carotene supplementation in shaping microbiome diversity post-release, and suggest inter-domain interactions may also only exert a minor influence. Further research on the function and ecology of skin bacterial and fungal microbiomes will be crucial for developing strategies to support survival of endangered amphibian species.

Adaptive EWMA and CUSUM charts with dynamic probability control limits

The utilization of dynamic probability control limits (DPCLs) can prove advantageous in the development of control charts, particularly in scenarios where sample sizes, areas of opportunity, or any other covariate values exhibit temporal fluctuations. In this study, we design the DPCLs-based two adaptive EWMA (AE) and adaptive CUSUM (AC) charts for monitoring the mean of a normally distributed process. We use Monte Carlo simulations to estimate the conditional false alarm rates, zero-state and steady-state (SS) average run-length profiles of the adaptive charts. It is found that the proposed AE and AC charts outperform the existing AE chart in terms of relative mean index. An example is given to demonstrate the implementation of the proposed charts.

Crowdsourcing the Mitigation of disinformation and misinformation: The case of spontaneous community-based moderation on Reddit

Community-based content moderation, an approach that utilises user-generated knowledge to shape the ranking and display of online content, is recognised as a potential tool in combating disinformation and misinformation. This study examines this phenomenon on Reddit, which employs a platform-wide content ranking system based on user upvotes and downvotes. By empowering users to influence content visibility, Reddit's system serves as a naturally occurring community moderation mechanism, providing an opportunity to analyse how users engage with this system. Focusing on discussions related to climate change, we observe that in this domain, low-credibility content is spontaneously moderated by Reddit users, although the magnitude of this effect varies across Subreddits. We also identify temporal fluctuations in content removal rates, indicating dynamic and context-dependent patterns influenced by platform policies and socio-political factors. These findings highlight the potential of community-based moderation in mitigating online false information, offering valuable insights for the development of robust social media moderation frameworks.

Generalized Lotka-Volterra Systems with Time Correlated Stochastic Interactions.

In this Letter, we explore the dynamics of species abundances within ecological communities using the generalized Lotka-Volterra (GLV) model. At variance with previous approaches, we present an analysis of GLV dynamics with temporal stochastic fluctuations in interaction strengths between species. We develop a dynamical mean field theory (DMFT) tailored for scenarios with colored noise interactions, which we term annealed disorder, and simple functional responses. Our DMFT framework enables us to show that annealed disorder acts as an effective environmental noise; i.e., every species experiences a time-dependent environment shaped by the collective presence of all other species. We then derive analytical predictions for the species abundance distribution that well match empirical observations. Our results suggest that annealed disorder in interaction strengths favors species coexistence and leads to a large pool of very rare species in the systems, supporting the insurance theory of biodiversity. This Letter offers new insights not only into the modeling of large ecosystem dynamics but also proposes novel methodologies for examining ecological systems.

Life cycle assessment of metal powder production: a Bayesian stochastic Kriging model-based autonomous estimation

Metal powder contributes to the environmental burdens of additive manufacturing (AM) substantially. Current life cycle assessments (LCAs) of metal powders present considerable variations of lifecycle environmental inventory due to process divergence, spatial heterogeneity, or temporal fluctuation. Most importantly, the amounts of LCA studies on metal powder are limited and primarily confined to partial material types. To this end, based on the data surveyed from a metal powder supplier, this study conducted an LCA of titanium and nickel alloy produced by electrode-inducted and vacuum-inducted melting gas atomization, respectively. Given that energy consumption dominates the environmental burden of powder production and is influenced by metal materials’ physical properties, we proposed a Bayesian stochastic Kriging model to estimate the energy consumption during the gas atomization process. This model considered the inherent uncertainties of training data and adaptively updated the parameters of interest when new environmental data on gas atomization were available. With the predicted energy use information of specific powder, the corresponding lifecycle environmental impacts can be further autonomously estimated in conjunction with the other surveyed powder production stages. Results indicated the environmental impact of titanium alloy powder is slightly higher than that of nickel alloy powder and their lifecycle carbon emissions are around 20 kg CO2 equivalency. The proposed Bayesian stochastic Kriging model showed more accurate predictions of energy consumption compared with conventional Kriging and stochastic Kriging models. This study enables data imputation of energy consumption during gas atomization given the physical properties and producing technique of powder materials.

Uncertainties of Economic Policy and Government Management Stability Played Important Roles in Increasing Suicides in Japan from 2009 to 2023.

Standardized suicide mortality rates per 100,000 (SMRs) in Japan consistently decreased from 2009 to 2019 but increased from 2020. The causes of these temporal SMR fluctuations remain to be clarified. Therefore, this study was conducted to identify the causalities underlying the recently transformed fluctuations of suicide mortality in Japan. Monthly suicide numbers disaggregated by sex and social standing, and political uncertainty indices, such as economic policy uncertainty (EPU) and government management instability (AENROP), were obtained from Japanese government databases. Interrupted time-series analysis was performed to analyze temporal fluctuations of SMRs disaggregated by sex/social standing associated with the three General Principles of Suicide Prevention Policy (GPSPP) periods and the COVID-19 pandemic. Panel data and vector autoregressive analyses were conducted to investigate causalities from political uncertainties to SMRs. During the first and second GPSPPs (2009-2017), all SMRs disaggregated by sex and social standing decreased, whereas those of unemployed females did not change. During the third GPSPP (2017-2022), decreasing trends in all SMRs were attenuated compared to previous periods. All female SMRs, except unemployed females, showed sharp increases synchronized with the pandemic outbreak. No male SMRs showed sharply increasing at the pandemic outbreak. SMRs of unemployed males/females drastically increased in the later periods of the pandemic, while SMRs of employed and multiple-person/single-person household males did not increase during the pandemic. SMR of unemployed males was positively related to AENROP but not EPU. Other male SMRs were positively related to EPU/AENROP. On the contrary, not all female SMRs were related to EPU/AENROP. Increasing AENROP generally contributed to increasing male SMRs throughout the observation period; however, susceptibility to AENROP and/or political information might have unexpectedly contributed to suppressing the sharply increasing male SMRs induced by large-scale social shocks (the COVID-19 pandemic outbreak) in Japan.

Monitoring the Biochemical Activity of Single Anammox Granules with Microbarometers.

Granule-based anaerobic ammonium oxidation (Anammox) is a promising biotechnology for wastewater treatments with extraordinary performance in nitrogen removal. However, traditional analytical methods often delivered an average activity of a bulk sample consisting of millions and even billions of Anammox granules with distinct sizes and components. Here, we developed a novel technique to monitor the biochemical activity of individual Anammox granules in real-time by recording the production rate of nitrogen gas with a microbarometer in a sealed chamber containing only one granule. It was found that the specific activity of a single Anammox granule not only varied by tens of folds among different individuals with similar sizes (activity heterogeneity) but also revealed significant breath-like dynamics over time (temporal fluctuation). Statistical analysis on tens of individuals further revealed two subpopulations with distinct color and specific activity, which were subsequently attributed to the different expression levels of heme c content and hydrazine dehydrogenase activity. This study not only provides a general methodology for various kinds of gas-producing microbial processes but also establishes a bottom-up strategy for exploring the structural-activity relationship at a single sludge granule level, with implications for developing a better Anammox process.