Quantification Of Dynamics Research Articles

Investigation of evolutionary dynamics for drug resistance in 3D spheroid model system using cellular barcoding technology.

Complex evolutionary dynamics governing the drug resistance is one of the major challenges in cancer treatment. Understanding these mechanisms requires a sequencing technology with higher resolution to delineate whether pre-existing or de novo drug mechanisms are behind the drug resistance. Combining this technology with clinically very relevant model system, namely 3D spheroids, better mimicking tumorigenesis and drug resistance have so far been lacking. Thus, we sought to establish dabrafenib and irinotecan resistant derivatives of barcoded 3D spheroids with the ultimate aim to quantify the selection-induced clonal dynamics and identify the genomic determinants in this model system. We found that dabrafenib and irinotecan induced drug resistance in 3D-HT-29 and 3D-HCT-116 spheroids are mediated by pre-existing and de novo resistant barcodes, indicating the presence of polyclonal drug resistance in this system. Moreover, whole-exome sequencing analysis found chromosomal gains and mutations associated with dabrafenib and irinotecan resistance in 3D-HT-29 and 3D-HCT-116 spheroids. Last, we show that dabrafenib and irinotecan resistance are also mediated by multiple drug resistance by detection of upregulation of the drug efflux pumps, ABCB1 and ABCG2, in our spheroid model system. Overall, we present the quantification of drug resistance and evolutionary dynamics in spheroids for the first time using cellular barcoding technology and the underlying genomic determinants of the drug resistance in our model system.

Pore-level Ostwald ripening of CO2 foams at reservoir pressure

The success of foam to reduce CO2 mobility in CO2 enhanced oil recovery and CO2 storage operations depends on foam stability in the reservoir. Foams are thermodynamically unstable, and factors such as surfactant adsorption, the presence of oil, and harsh reservoir conditions can cause the foam to destabilize. Pore-level foam coarsening and anti-coarsening mechanisms are not, however, fully understood and characterized at reservoir pressure. Using lab-on-a-chip technology, we probe dense (liquid) phase CO2 foam stability and the impact of Ostwald ripening at 100 bars using dynamic pore-scale observations. Three types of pore-level coarsening were observed: (1) large bubbles growing at the expense of small bubbles, at high aqueous phase saturations, unrestricted by the grains; (2) large bubbles growing at the expense of small bubbles, at low aqueous phase saturation, restricted by the grains; and (3) equilibration of plateau borders. Type 3 coarsening led to stable CO2 foam states eight times faster than type 2 and ten times faster than type 1. Anti-coarsening where CO2 diffused from a large bubble to a small bubble was also observed. The experimental results also compared stabilities of CO2 foam generated with hybrid nanoparticle–surfactant solution to CO2 foam stabilized by only surfactant or nanoparticles. Doubling the surfactant concentration from 2500 to 5000 ppm and adding 1500 ppm of nanoparticles to the 2500 ppm surfactant-based solution resulted in stronger foam, which resisted Ostwald ripening. Dynamic pore-scale observations of dense phase CO2 foam revealed gas diffusion from small, high-curvature bubbles to large, low-curvature bubbles and that the overall curvature of the bubbles decreased with time. Overall, this study provides in situ quantification of CO2 foam strength and stability dynamics at high-pressure conditions.Article HighlightsA comprehensive laboratory investigation of CO2 foam stability and the impact of Ostwald ripening.Pore-level foam coarsening and anti-coarsening mechanisms insights.

Dynamic quantification of on-road emissions in Hong Kong: Impact from traffic congestion and fleet composition variation

Policymakers need a good understanding of on-road air pollutant emission patterns to manage air quality effectively, but it can be challenging to dynamically capture emission patterns with high spatial and temporal resolutions in cities. In this study, we used traffic congestion index extracted from Google Map, traffic density model and local emission factors to develop a dynamic on-road emission inventory for Hong Kong. The pollutants include NOx, CO, NMVOC, CO2, PM2.5, PM10, and CH4. On weekdays, exhaust emissions increased by up to 50.0% compared with those on Sunday due to higher traffic congestion index. High emission rates were observed on highways during weekday evening peak hours and shifted to regional centers on Sunday evenings. Public light buses, goods vehicles, and taxis were the most significant contributors of NMVOC, NOx, and CO emissions, respectively. Control policy testing showed that replacing diesel-fueled buses with electric buses could significantly reduce NOx and PM exhaust emissions, whereas replacing liquefied petroleum gas fueled public light buses with electric buses could efficiently control NMVOC emissions. Eliminating private cars older than 15 years could effectively reduce CO emissions. This study provides a dynamic on-road emission inventory for Hong Kong with high temporal and spatial resolutions. The results can be extended to real-time air quality simulations and human exposure research in the future.

Hot-Electron Dynamics Mediated Medical Diagnosis and Therapy.

Surface plasmon resonance excitation significantly enhances the absorption of light and increases the generation of "hot" electrons, i.e., conducting electrons that are raised from their steady states to excited states. These excited electrons rapidly decay and equilibrate via radiative and nonradiative damping over several hundred femtoseconds. During the hot-electron dynamics, from their generation to the ultimate nonradiative decay, the electromagnetic field enhancement, hot electron density increase, and local heating effect are sequentially induced. Over the past decade, these physical phenomena have attracted considerable attention in the biomedical field, e.g., the rapid and accurate identification of biomolecules, precise synthesis and release of drugs, and elimination of tumors. This review highlights the recent developments in the application of hot-electron dynamics in medical diagnosis and therapy, particularly fully integrated device techniques with good application prospects. In addition, we discuss the latest experimental and theoretical studies of underlying mechanisms. From a practical standpoint, the pioneering modeling analyses and quantitative measurements in the extreme near field are summarized to illustrate the quantification of hot-electron dynamics. Finally, the prospects and remaining challenges associated with biomedical engineering based on hot-electron dynamics are presented.

A Fast and Reversible Responsive Bionic Transmembrane Nanochannel for Dynamic Single-Cell Quantification of Glutathione.

Biological channels can rapidly and continuously modulate ion transport behaviors in response to external stimuli, which play essential roles in manipulating physiological and pathological processes in cells. Here, to mimic the biological channels, a bionic nanochannel is developed by synergizing a cationic silicon-substituted rhodamine (SiRh) with a glass nanopipette for transmembrane single-cell quantification. Taking the fast and reversible nucleophilic addition reaction between glutathione (GSH) and SiRh, the bionic nanochannel shows a fast and reversible response to GSH, with its inner-surface charges changing between positive and negative charges, leading to a distinct and reversible switch in ionic current rectification (ICR). With the bionic nanochannel, spatiotemporal-resolved operation is performed to quantify endogenous GSH in a single cell, allowing for monitoring of intracellular GSH fluctuation in tumor cells upon photodynamic therapy and ferroptosis. Our results demonstrate that it is a feasible tool for in situ quantification of the endogenous GSH in single cells, which may be adapted to addressing other endogenous biomolecules in single cells by usage of other stimuli-responsive probes.

Transformation of Brazil's biomes: The dynamics and fate of agriculture and pasture expansion into native vegetation

Land use and cover change (LUCC) in Brazil encompass a complex interplay of diverse factors across different biomes. Understanding these dynamics is crucial for informed decision-making and sustainable land management. In this study, we comprehensively analyzed LUCC patterns and drivers using 30 m resolution MapBiomas Collection 6.0 data (1985–2020). By mapping deforestation of primary and secondary natural vegetation, natural vegetation regeneration, and transitions between pasture, soybean, agriculture, and irrigation, we shed light on the intricate nature of LUCC in Brazil. Our findings highlight significant and increasing trends of deforestation in primary vegetation in the country. Simultaneously, the Atlantic Forest, Caatinga, Pampa, and other regions of the Cerrado have experienced intensification processes. Notably, the pasture area in Brazil reached its peak in 2006 and has since witnessed a gradual replacement by soybean and other crops. While pasture-driven deforestation persists in most biomes, the net pasture area has only increased in the Amazon and Pantanal, decreasing in other biomes due to the conversion of pasturelands to intensive cropping in other regions. Our analysis further reveals that primary and secondary vegetation deforestation accounts for a substantial portion of overall forest loss, with 72 % and 17 %, respectively. Of the cleared areas, 48 % were in pasture, 9 % in soybean cultivation, and 16 % in other agricultural uses in 2020. Additionally, we observed a lower rate of deforestation in the Atlantic Forest, a biome that has been significantly influenced by anthropogenic activities since 1986. This holistic quantification of LUCC dynamics provides a solid foundation for understanding the impacts of these changes on local to continental-scale land-atmosphere interactions. By unraveling the complex nature of LUCC in Brazil, this study aims to contribute to the development of effective strategies for sustainable land management and decision-making processes.

The relevance of a right scale for sampling when studying high-resolution behavioral dynamics

Many species used in behavioral studies are small vertebrates with high metabolic rates and potentially enhanced temporal resolution of perception. Nevertheless, the selection of an appropriate scales to evaluate behavioral dynamics has received little attention. Herein, we studied the temporal organization of behaviors at fine-grain (i.e. sampling interval ≤1s) to gain insight into dynamics and to rethink how behavioral events are defined. We statistically explored high-resolution Japanese quail (Coturnix japonica) datasets encompassing 17 defined behaviors. We show that for the majority of these behaviors, events last predominately <300ms and can be shorter than 70ms. Insufficient sampling resolution, even in the order of 1s, of behaviors that involve spatial displacement (e.g. walking) yields distorted probability distributions of event durations and overestimation of event durations. Contrarily, behaviors without spatial displacement (e.g. vigilance) maintain non-Gaussian, power-law-type distributions indicative of long-term memory, independently of the sampling resolution evaluated. Since data probability distributions reflect underlying biological processes, our results highlight the importance of quantification of behavioral dynamics based on the temporal scale pertinent to the species, and data distribution. We propose a hierarchical model that links diverse types of behavioral definitions and distributions, and paves the way towards a statistical framework for defining behaviors.

Determination and Chemotaxonomic Analysis of Lanostane Triterpenoids in the Mycelia of Ganoderma spp. Using Ultra-performance Liquid Chromatography-Tandem Mass Spectrometry (I).

A comprehensive and sensitive method combining ultra-performance liquid chromatography with tandem mass spectrometry was developed for the quantification of characteristic triterpenoids in Ganoderma mycelia. Eight ganoderic acids previously isolated from the mycelia of Ganoderma lingzhi were separated with a binary mobile phase on a reversed-phase C18 column. A triple quadrupole mass spectrometer equipped with an electrospray ionization source was used as the detector in the negative ion mode. Identification and quantitation of target ganoderic acids were accomplished using the dynamic multiple reaction monitoring mode. The developed method was validated in terms of linearity, precision, accuracy, stability, and recovery. The method was first applied to quantify the contents of eight ganoderic acids in the mycelia of G. lingzhi at different times to determine the optimum fermentation conditions. Subsequently, the distribution of triterpenoids and the contents of eight ganoderic acids in sixteen different Ganoderma species were investigated. The results indicated that UV chromatography combined with dynamic multiple reaction monitoring quantification was an effective chemotaxonomy method for Ganoderma species identification. This study also provided a helpful analytical methodology for both scientific and industrial applications in the quality control of Ganoderma triterpenoids.

Multiscale imaging and quantitative analysis of plasma membrane protein-cortical actin interplay

The spatiotemporal organization of cell surface receptors is important for cell signaling. Cortical actin (CA), the subset of the actin cytoskeleton subjacent to the plasma membrane (PM), plays a large role in cell surface receptor organization. However, this has been shown largely through actin perturbation experiments, which raise concerns of nonspecific effects and preclude quantification of actin architecture and dynamics under unperturbed conditions. These limitations make it challenging to predict how changes in CA properties can affect receptor organization. To derive direct relationships between the architecture and dynamics of CA and the spatiotemporal organization of PM proteins, including cell surface receptors, we developed a multiscale imaging and computational analysis framework based on the integration of single-molecule imaging (SMI) of PM proteins and fluorescent speckle microscopy (FSM) of CA (combined: SMI-FSM) in the same live cell. SMI-FSM revealed differential relationships between PM proteins and CA based on the PM proteins’ actin binding ability, diffusion type, and local CA density. Combining SMI-FSM with subcellular region analysis revealed differences in CA dynamics that were predictive of differences in PM protein mobility near ruffly cell edges versus closer to the cell center. SMI-FSM also highlighted the complexity of cell-wide actin perturbation, where we found that global changes in actin properties caused by perturbation were not necessarily reflected in the CA properties near PM proteins, and that the changes in PM protein properties upon perturbation varied based on the local CA environment. Given the widespread use of SMI as a method to study the spatiotemporal organization of PM proteins and the versatility of SMI-FSM, we expect it to be widely applicable to enable future investigation of the influence of CA architecture and dynamics on different PM proteins, especially in the context of actin-dependent cellular processes.

Electrocortical activity correlated with locomotor adaptation during split-belt treadmill walking.

Locomotor adaptation is crucial for daily gait adjustments to changing environmental demands and obstacle avoidance. Mobile brain imaging with high-density electroencephalography (EEG) now permits quantification of electrocortical dynamics during human locomotion. To determine the brain areas involved in human locomotor adaptation, we recorded high-density EEG from healthy, young adults during split-belt treadmill walking. We incorporated a dual-electrode EEG system and neck electromyography to decrease motion and muscle artefacts. Voluntary movement preparation and execution have been linked to alpha (8-13Hz) and beta band (13-30Hz) desynchronizations in the sensorimotor and posterior parietal cortices, whereas theta band (4-7Hz) modulations in the anterior cingulate have been correlated with movement error monitoring. We hypothesized that relative to normal walking, split-belt walking would elicit: (1) decreases in alpha and beta band power in sensorimotor and posterior parietal cortices, reflecting enhanced motor flexibility; and (2) increases in theta band power in anterior cingulate cortex, reflecting instability and balance errors that will diminish with practice. We found electrocortical activity in multiple regions that was associated with stages of gait adaptation. Data indicated that sensorimotor and posterior parietal cortices had decreased alpha and beta band spectral power during early adaptation to split-belt treadmill walking that gradually returned to pre-adaptation levels by the end of the adaptation period. Our findings emphasize that multiple brain areas are involved in adjusting gait under changing environmental demands during human walking. Future studies could use these findings on healthy, young participants to identify dysfunctional supraspinal mechanisms that may be impairing gait adaptation. KEY POINTS: Identifying the location and time course of electrical changes in the brain correlating with gait adaptation increases our understanding of brain function and provides targets for brain stimulation interventions. Using high-density EEG in combination with 3D biomechanics, we found changes in neural oscillations localized near the sensorimotor, posterior parietal and cingulate cortices during split-belt treadmill adaptation. These findings suggest that multiple cortical mechanisms may be associated with locomotor adaptation, and their temporal dynamics can be quantified using mobile EEG. Results from this study can serve as a reference model to examine brain dynamics in individuals with movement disorders that cause gait asymmetry and reduced gait adaptation.

Dynamic modeling and uncertainty quantification of district heating systems considering renewable energy access

The district heating system (DHS) is one of the essential carriers for coordinating renewable energy with fossil energy and achieving flexible energy consumption. Considering the uncertainty of both the renewable energy units' power and the consumers' aggregate loads impact the transport process of the DHS, the thermal characteristics of the DHS transportation and the node temperature response under the source-load uncertainty need to be quantified and analyzed. This paper proposed a method to calculate the nodes' temperature dynamic response of the DHS under multiple source-load uncertainty scenarios. The prediction and combination methods of supply and demand probability intervals of source and load nodes under varying credibility levels were investigated. Additionally, the effects of source-load uncertainty boundary conditions on the DHS transport process were analyzed. The temperature response of the nodes was calculated based on the established dynamic DHS model under multiple source-load uncertainty scenarios. A DHS in Beijing was selected for case validation and analysis, and it has 90 nodes and 109 pipes. The simulation results showed that the relative average error of node flow rate in the dynamic DHS model was 3.02%, and the average difference in node temperature was 1 °C. The probability distribution semi-analytical method and the Bayesian credible interval calculation method can accurately describe the uncertainty on both source and load sides. The models and algorithms proposed in this paper could effectively quantify the nodes' dynamic temperature response under the source-load uncertainty.

Theoretical verification on adsorptive removal of caffeine by carbon and nitrogen-based surfaces: Role of charge transfer, π electron occupancy, and temperature

Eliminating an emerging water pollutant, caffeine molecules, from an aqueous solution using carbon and nitrogen-based adsorbents is of significant interest to public health. These adsorbents have been shown to have decent adsorption capacity toward caffeine due to their surface functionality. Therefore, screening various carbon and nitrogen-based surfaces can be a better option for high-performance adsorbents to remove caffeine efficiently from wastewater. Herein, we present combined first principles and molecular dynamics quantification of the adsorption enthalpies of caffeine molecules on the possible active sites of carbon and nitrogen-based adsorbents (graphene, phagraphene, graphdiyne, single-wall carbon nanotube, fullerene, and graphitic carbon nitride) with the incorporation of Van der Waals interactions. From the DFT calculations, N-doped carbon surfaces show the highest adsorption energies of single and dimer CAF compared to pristine carbon-based adsorbents. A charge density difference and Bader charge analysis display that high charge transfer occurs between the caffeine's oxygen and the surface's nitrogen atoms. An abundance of π-electrons from the nitrogen atoms, composed of large electron clouds of aromatic rings on the graphitic carbon surface, tends to favor extensive π-π interactions with the caffeine molecule. The high value of pz electron occupancy (1.445) of N in the hexagonal ring of the graphitic surface transfers additional charge transfer, which leads to strong adsorption energy of CAF than pristine surfaces. Also, the g-C3N4 surface adsorbs the CAF molecule with higher adsorption than other N-doped carbon surfaces due to the high pz_eo (1.5448) of N atoms on the surface. At 310 K, the water molecules' kinetics aids the single and dimer caffeine molecules to adsorb with the highest adsorption energies on the active sites of g-C3N4 surfaces than graphene adsorbent.

Quantification of soil erosion dynamics in the hilly red soil region of Southeast China based on exposed roots

The hilly red soil region (HRSR) of southeastern China is one of the most eroded areas in China. However, the knowledge on regional erosion dynamics and climate drivers still remains fragmental due to lack of long-term observations. Herein, exposed roots from endemic Pinus massoniana, Cunningh lanceolata, and Cinnamomum camphora trees were consulted to explore the evolution of local soil erosion across time and its linkages with climates. Results demonstrated that soil loss induced a distinct diminution in the lumen area of earlywood tracheids/vessels in the exposed roots. Based upon the signals in these root-ring records, we reconstructed average erosion rates for three sheet-erosion sites and found that local eroding conditions have been mitigated after decades-long protection. In addition, we built a chronology of sudden erosive activities since 1971 at the Benggang sites in HRSR with exposed roots. The El Niño-Southern Oscillation (ENSO) acted as a critical trigger for erosive events via modulations of spring-summer precipitation. Ongoing climate change may strongly modulate local soil erosion and an adjustment of mitigation strategies is highly desired to enhance climate change adaption for soil conservation.

Data-driven approach to dynamic uncertainty quantification of oil well production: Decline curve analysis and Hidden Markov Model

The upstream oil and gas industry has already used decline curve analysis to forecast oil flow rates and estimate the ultimate recovery for decades. Despite its popularity, the applications are theoretically limited and could not handle irregular variations in oil well flow behavior. In other words, it is a method for predicting the general behavior of oil wells rather than providing reliable information in specifics. It is mostly irritated by subjective implementation and a single offer of a solution without uncertainty quantification.The Hidden Markov Model (HMM) was employed in this work for dynamic uncertainty quantification in oil well flow forecast and estimated ultimate recovery (EUR). It is performed by determining the set of appropriate decline curves along with the corresponding probabilities. HMM has three main parameters: the probability transition matrix (A), the emission probability matrix (B), and the prior distribution. All these parameters can be learned from data using the Baum-Welch algorithm. The trained HMM was utilized to generate the posterior. Accordingly, using HMM and learning from the data, a dynamic probabilistic decline curve analysis is conducted. It preserves the decline curve's strength of simplicity while also covering the flaws of subjectivity and lack of uncertainty quantification. The novelty of our study is that it focuses on dynamic uncertainty quantification of oil production forecasts using new observations and covers any regular or irregular (planed or naturally stochastic) variations of flow trends. We implemented the method in cases with the variation of the flow rate by choke opening and also a sharp decline in the late time, which are not covered by decline curve analysis and not proposed in previous studies. Dynamic predictions by HMM followed the variations in flow patterns obtained by the new observations. In addition, the calculated data-driven probability distribution of EUR acceptably covers the true values.

Semi-automated quantification of tricuspid valve dynamics and structure in tetralogy of Fallot and hypoplastic left heart syndrome using three-dimensional echocardiography

Anomalies of the tricuspid valve (TV) are associated with worsened prognosis in congenital heart disease (CHD). Here, we present a descriptive study examining changes in TV morphology in two CHD conditions—repaired tetralogy of Fallot (rTOF) and hypoplastic left heart syndrome (HLSH), using three-dimensional echocardiography. Full volume acquisitions of the TV and right ventricle (RV) were performed from an RV-focused apical view using ECG gating over 2–5 consecutive cardiac cycles using 3D echocardiography, from which TV annulus and leaflet parameters were quantified. A total of 40 rTOF patients (age 14 ± 9.8 years), 40 HLHS patients (age1.0 ± 1.5 years) and 80 age and gender matched controls were included. Among leaflet parameters, antero-posterior and posterior-septal TV coaptation heights were smaller in rTOF (p < 0.001) vs. control. Conversely, only the short-axis TV height was different in HLHS vs. controls (HLHS 1.6 ± 0.4 cm vs. control 1.4 ± 0.3 cm). TV leaflet parameters tended to be larger in HLHS, while leaflet coaptation distances were similar between groups. We demonstrate that 3D echocardiography for assessment of the TV is feasible in rTOF and HLHS patients and identifies unique differences in TV morphology. Future studies should clarify the clinical significance of TV morphology in these patient populations.

Hybrid Eulerian–Lagrangian framework for structural full-field vibration quantification and modal shape visualization

Vibration measurement and modal analysis under a high spatial and temporal resolution have high significance for revealing inner structural characteristics. This work develops a hybrid Eulerian–Lagrangian framework for structural full-field dynamic vibration quantification and modal shape visualization and validates its performance through vision-based vibration experiments. By assuming a linear vibration system, modal responses that are estimated from the vibration signals on a structural surface provide a reliable basis for manipulating temporal and spatial vibrations in Eulerian motion processing. Then, dynamic spatial motion can be quantified by using the improved non-rigid image registration algorithm. Meanwhile, from the Lagrangian perspective, high-quality visualization of modal shapes is implemented by compensating spatial motion according to magnified dense displacement fields. The experiment results showed that the proposed hybrid framework has superiority in controlled modal tests and has potential for more practical applications.

New links between invariant dynamical structures and uncertainty quantification

This paper proposes a new uncertainty measure, appropriate for quantifying the performance of transport models in assessing the origin or source of a given observation. It is found that in a neighborhood of the observation the proposed uncertainty measure is related to the invariant dynamical structures of the model. The paper illustrates the implementation of the proposed definition to quantify the performance of ocean data sets in the context of a real oil spill event in the Eastern Mediterranean in 2021.

Dynamic Modeling of Carbon Dioxide Transport through the Skin Using a Capnometry Wristband.

The development of a capnometry wristband is of great interest for monitoring patients at home. We consider a new architecture in which a non-dispersive infrared (NDIR) optical measurement is located close to the skin surface and is combined with an open chamber principle with a continuous circulation of air flow in the collection cell. We propose a model for the temporal dynamics of the carbon dioxide exchange between the blood and the gas channel inside the device. The transport of carbon dioxide is modeled by convection-diffusion equations. We consider four compartments: blood, skin, the measurement cell and the collection cell. We introduce the state-space equations and the associated transition matrix associated with a Markovian model. We define an augmented system by combining a first-order autoregressive model describing the supply of carbon dioxide concentration in the blood compartment and its inertial resistance to change. We propose to use a Kalman filter to estimate the carbon dioxide concentration in the blood vessels recursively over time and thus monitor arterial carbon dioxide blood pressure in real time. Four performance factors with respect to the dynamic quantification of the CO2 blood concentration are considered, and a simulation is carried out based on data from a previous clinical study. These demonstrate the feasibility of such a technological concept.

Combining Multi-Source Data and Feature Optimization for Plastic-Covered Greenhouse Extraction and Mapping Using the Google Earth Engine: A Case in Central Yunnan Province, China

Rapidly increasing numbers of the plastic-covered greenhouse (PCG) worldwide ensure food security but threaten environmental security; thus, accurate monitoring of the spatiotemporal pattern in plastic-covered greenhouses (PCGs) is necessary for modern agricultural management and environmental protection. However, many urgent issues still exist in PCG mapping, such as multi-source data combination, classification accuracy improvement, spatiotemporal scale expansion, and dynamic trend quantification. To address these problems, this study proposed a new framework that progressed layer by layer from multi-feature scenario construction, classifier and feature scenario preliminary screening, feature optimization, and spatiotemporal mapping, to rapidly identify large-scale PCGs by integrating multi-source data using Google Earth Engine (GEE), and the framework was first applied to Central Yunnan Province (CYP), where PCGs are concentrated but no relevant research exists. The results suggested that: (1) combining the random forest (RF) classifier and spectrum (S) + backscatter (B) + index (I) + texture (T) + terrain (Tr) feature scenario produced the highest F-score (95.60%) and overall accuracy (88.04%). (2) The feature optimization for the S + I + T + B + Tr scenario positively impacted PCG recognition, increasing the average F-score by 1.03% (96.63% vs. 95.60%). (3) The 6-year average F-score of the PCGs extracted by the combined RF algorithm and the optimal feature subset exceeded 95.00%, and its spatiotemporal mapping results indicated that PCGs were prominently agglomerated in the central CYP and continuously expanded by an average of 65.45 km2/yr from 2016 to 2021. The research reveals that based on the GEE platform, multi-source data can be integrated through a feature optimization algorithm to more efficiently map PCG spatiotemporal information in complex regions.

High-Throughput Analysis of Neutrophil Extracellular Trap Levels in Subtypes of People with Type 1 Diabetes.

Neutrophils might play an important role in the pathogenesis of autoimmune diseases, including type 1 diabetes (T1D), by contributing to immune dysregulation via a highly inflammatory program called neutrophil extracellular trap (NET) formation or NETosis, involving the extrusion of chromatin entangled with anti-microbial proteins. However, numerous studies reported contradictory data on NET formation in T1D. This might in part be due to the inherent heterogeneity of the disease and the influence of the disease developmental stage on neutrophil behavior. Moreover, there is a lack of a standardized method to measure NETosis in an unbiased and robust manner. In this study, we employed the Incucyte® ZOOM live-cell imaging platform to study NETosis levels in various subtypes of adult and pediatric T1D donors compared to healthy controls (HC) at baseline and in response to phorbol-myristate acetate (PMA) and ionomycin. Firstly, we determined that the technique allows for an operator-independent and automated quantification of NET formation across multiple time points, which showed that PMA and ionomycin induced NETosis with distinct kinetic characteristics, confirmed by high-resolution microscopy. NETosis levels also showed a clear dose-response curve to increasing concentrations of both stimuli. Overall, using Incucyte® ZOOM, no aberrant NET formation was observed over time in the different subtypes of T1D populations, irrespective of age, compared to HC. These data were corroborated by the levels of peripheral NET markers in all study participants. The current study showed that live-cell imaging allows for a robust and unbiased analysis and quantification of NET formation in real-time. Peripheral neutrophil measures should be complemented with dynamic quantification of NETing neutrophils to make robust conclusions on NET formation in health and disease.