Slow Changes Research Articles

Mixed Adsorption Mono- and Multilayers of ß-Lactoglobulin Fibrils and Sodium Polystyrene Sulfonate

The formation of beta-lactoglobulin (BLG)/sodium polystyrene sulfonate (PSS) complexes decelerates the change in the surface properties of the mixed solutions with the surface age and increases the steady-state dilational surface elasticity in a narrow PSS concentration range. At the same time, the changes in the surface properties are accelerated in the dispersions of BLG fibrils with and without PSS due to the influence of small peptides coexisting with fibrils. A decrease in the peptide concentration as a result of the dispersion purification leads to slower changes in the surface properties at low PSS concentrations. The increase in the polyelectrolyte concentration results in an increase in the steady-state surface elasticity due to the fibril/PSS complex formation and in very slow changes in the surface properties if the polyelectrolyte exceeds a certain critical value. The latter effect is a consequence of the formation of large aggregates and of an increase in the electrostatic adsorption barrier. The consecutive adsorption of BLG fibrils and PSS leads to the formation of regular multilayers at the liquid–gas interface. The multilayer properties change noticeably with an increase in the number of layers from four to six in agreement with previous results on the multilayers of PSS with an oppositely charged synthetic polyelectrolyte, presumably due to the heterogeneity of the first PSS layer. The dynamic elasticity of the multilayers approaches 250 mN/m, indicating that they can effectively stabilize foams and emulsions.

Wilson disease: Novel Diagnostic and Therapeutic Approaches.

The Wilson disease (WD) research field is rapidly evolving, and new diagnostic and therapeutical approaches are expected to be change-gamers in the disease for the incoming years, after decades of slow changing options. Non-ceruloplasmin bound copper assays for circulating bioavailable copper are being tested for use in monitoring therapy and may also help in the diagnosis of new cases of WD. Other diagnostic advances include use of quantitative detection of ATP7B peptides in dried blood spots, a method that is being tested for use in newborn screening for WD, and the use of metallothionein immuno-staining of liver biopsy specimens to differentiate WD from other liver diseases. Ongoing and future trials of gene therapy and use of methanobactin are expected to restore biliary copper excretion from the liver, thus making a cure for WD a plausible therapeutic objective. With the aim of helping updating physicians, this review summarizes the novel methods for WD diagnosis and future therapies. Advancing understanding of the scientific advances that can be applied to WD will be critical for ensuring that our patients will receive the best current and future care.

Searching for Stellar Activity Cycles Using Flares: The Short- and Long-timescale Activity Variations of TIC-272272592

We examine 4 yr of Kepler 30 minutes data, and five sectors of Transiting Exoplanet Survey Satellite 2 minutes data for the dM3 star KIC-8507979/TIC-272272592. This rapidly rotating (P = 1.2 day) star has previously been identified as flare active, with a possible long-term decline in its flare output. Such slow changes in surface magnetic activity are potential indicators of solar-like activity cycles, which can yield important information about the structure of the stellar dynamo. We find that while TIC-272272592 shows evidence for both short- and long-timescale variations in its flare activity, it is unlikely physically motivated. Only a handful of stars have been subjected to such long-baseline point-in-time flare studies, and we urge caution in comparing results between telescopes due to differences in bandpass, signal-to-noise ratio, and cadence. In this work, we develop an approach to measure variations in the flare frequency distributions over time, which is quantified as a function of the observing baseline. For TIC-272272592, we find a 2.7σ detection of a sector which has a flare deficit, therefore indicating the short-term variation could be a result of sampling statistics. This quantifiable approach to describing flare-rate variation is a powerful new method for measuring the months-to-years changes in surface magnetic activity, and provides important constraints on activity cycles and dynamo models for low-mass stars.

Nonlinear second order plus time delay model identification and nonlinear PID controller tuning based on extended linearization method

PID control systems based on the first order plus time delay model (FOPTD), which approximate the full system dynamics, are well-accepted for a wide range of linear processes. While such controllers can be applied to overdamped nonlinear processes, they often experience excessive overshoots and oscillations for general nonlinear processes. To overcome this limitation, we propose a novel method to design a nonlinear PID controller based on the second order plus time delay (SOPTD) model. The system nonlinearity requires parameter adjustments of the linearized model across operational ranges. Hence, in this work, it is handled by the extended linearization method (ELM), ensuring local stability under the assumptions of slow and small changes in operating points. Importantly, the model achieves global input-to-output stability even without the above constraints, provided there are no structural and parametric errors. The resulting nonlinear SOPTD model can describe changes in process gain and two time constants as the operation point varies. We demonstrate the applicability of our approach with a polymerization reactor simulation and liquid-level control experiments.

Leveraging AI to Automate Detection and Quantification of Extrachromosomal DNA (ecDNA) to Decode Drug Responses.

Traditional drug discovery efforts have largely focused on targeting rapid, reversible protein-mediated adaptations to undermine cancer cells' resistance to therapy. However, cancer cells also exploit DNA-based strategies, typically viewed as slow, irreversible, and unpredictable changes like point mutations or the selection of drug-resistant clones. Contrary to this perception, extrachromosomal DNA (ecDNA) represents a form of DNA alteration that is rapid, reversible, and predictable, playing a crucial role in cancer's adaptive response. In this study, we present a novel post-processing pipeline for the automated detection and quantification of ecDNA in Fluorescence in situ Hybridization (FISH) images using the Microscopy Image Analyzer (MIA) tool. Our approach is particularly designed to monitor ecDNA dynamics during drug treatment, providing a quantitative framework to understand how ecDNA enables cancer cells to swiftly and reversibly adapt to therapeutic pressure. This pipeline not only offers a valuable resource for researchers aiming to automate ecDNA detection in FISH images but also sheds light on the adaptive mechanisms of ecDNA in response to epigenetic remodeling agents like JQ1.

Experimental study on the quasi-static tensile mechanical response of PTFE from 77 K to 293 K

PTFE’s exceptional low-temperature resistance makes it an ideal material for cryogenic aerospace fuel seals. However, the current understanding of its mechanical behaviors at low temperatures remains limited, posing potential design vulnerabilities. This study investigates the quasi-static mechanical properties of PTFE over a temperature range from 77 K to room temperature, with a particular focus on the effects of temperature and strain rate, which are key variables in sealing applications. The experimental conditions are categorized into ambient temperature (293 K), conventional low temperatures (233 K–293 K), and cryogenic temperatures (77 K–223 K). Testing was conducted at crosshead speeds of 1, 10, and 100 mm/min to evaluate their influence. The results reveal significant phase transition characteristics in PTFE’s low-temperature mechanical properties, with the effects of phase transitions far outweighing those of strain rate, leading to dramatic changes in the stress response, from elasticity through plasticity to fracture. Further analysis indicates that these effects are concentrated around the transition state temperature zone, with slower changes observed at other temperatures. This study highlights the critical importance of considering phase transitions in the low-temperature design of PTFE structures.

Variable Ionized Disk Winds in MAXI J1803−298 Revealed by NICER

We present the results from the NICER observation data of MAXI J1803−298 across the entire 2021 outburst. In the intermediate and soft state, we detect significant absorption lines at ∼7.0 and ∼6.7 keV, arising from X-ray disk winds outflowing with a velocity of hundreds of km s−1 along our line of sight. The fitting results from the photoionized model suggest that the winds are driven by thermal pressure and the mass-loss rate is low. We find a clear transition for iron from predominantly H-like to predominantly He-like during the intermediate-to-soft state transition. Our results indicate this transition for iron is caused by the evolution of the illuminating spectrum and the slow change of the geometric properties of the disk winds together. The coexistence of disk winds and quasiperiodic oscillation features in the intermediate state is also reported. Our study makes MAXI J1803−298 the first source in which a transition from optical winds to X-ray winds is detected, offering new insights into the evolution of disk winds across an entire outburst and long-term coupling of accretion disks and mass outflows around accreting black holes.

Catching Mind Wandering With Pupillometry: Conceptual and Methodological Challenges.

Mind-wandering (MW) refers to the shift of attention away from an ongoing task and/or external environment towards mental contents (e.g., memories, prospective thoughts) unrelated to the task. Physiological measures (e.g., pupil size, EEG, and fMRI) have often been acquired as objective markers for MW states, which has greatly helped their study as well as triangulation with other measures. Pupillometry in particular has been used as a covert biomarker of MW because it is reliably modulated by several distinct processes spanning arousal, emotion, and attention, and it signals attentional lapses. Yet, coupling MW and the measurement of pupil size has led to seemingly contrasting results. We argue that, common to the studies reviewed here, one reason is resolving to the measurement of tonic pupil size, which reflects low-frequency, slow changes in one's physiological state, and thus implicitly assumes that MW is a static, long-lasting process. We then additionally focus on three major axes of variability in the reviewed studies: (i) the definition and measurement of MW; (ii) the impact of contextual aspects, such as task demands and individual arousal levels; (iii) the identification and tracking of MW in combination with pupillary measures. We provide an overview of these differences and put forward recommendations for using physiological measures-including, but not limited to, pupil size-in MW research effectively. In conclusion, pupillometry can be a very informative tool for MW research, provided that it is used with the due methodological caution.

Metabolic interplay drives population cycles in a cross-feeding microbial community.

Population cycles are prevalent in ecosystems and play key roles in determining their functions 1,2 . While multiple mechanisms have been theoretically shown to generate population cycles 3-6 , there are limited examples of mutualisms driving self-sustained oscillations. Using an engineered microbial community that cross-feeds essential amino acids, we experimentally demonstrate cycles in strain abundance that are robust across environmental conditions. A nonlinear dynamical model that incorporates the experimentally observed cross-inhibition of amino acid production recapitulates the population cycles. The model shows that the cycles represent internally generated relaxation oscillations, which emerge when fast resource dynamics with positive feedback drive slow changes in strain abundance. Our findings highlight the critical role of resource dynamics and feedback in shaping population cycles in microbial communities and have implications for biotechnology.

Assessment of the influence of climatic factors on the state of terrestrial ecosystems in the Northwestern region of Russia

The article analyzes the change of climatic conditions in the Northwest of Russia, including the characteristics of dangerous hydrometeorological events (cold and heat waves, strong winds, extreme rainfall, snowfall, ice-frost deposits, hail) and slow climatic changes (increase in the number of days with the transition of air temperature through 0°С, coastal abrasion) in connection with their negative impact on terrestrial ecosystems. It was found that the influence of meteorological and climatic factors on terrestrial ecosystems is most pronounced in the northern part of the studied region, especially on the coast of the Barents Sea. Towards the south, the values of all indicators gradually decrease, and their structure changes. In the northern part of the study area (Murmansk and Arkhangelsk oblasts, the Nenets Autonomous Okrug), phenomena associated with strong winds and intensive ice-frost deposition, which contribute to the formation of an ice crust on the Earth’s surface, prevail. As one moves away from the coast, severe frost is observed more often (Komi Republic). In the center and south of the region, heavy rainfall, severe frost, and intense heat are the most frequent, resulting in a high fire hazard. The study carried out the ranking of the subjects of the Northwestern Federal District according to the degree of intensity of this process. Comprehensive assessments of the negative impact of changing climatic conditions on terrestrial ecosystems can be used to make decisions on the development of a strategy for environmental security of the regions of the Russian Federation.

Development of ITER first wall heat load feedback control

Real-time monitoring and control of thermal loads on tungsten plasma-facing components is mandatory for ITER to avoid their overheating during plasma discharges. For the Start of Research Operations (SRO) phase, dedicated First Wall Heat Load Control (FWHLC) functions are included in the ITER Plasma Control System (PCS) to prevent the development of excessive plasma heat loads on the inertially cooled first wall (FW). In this work, we propose a FWHLC design with a model-based approach, which features a simplified thermal model for the FW armour. This control-oriented model simulates the thermal response of ITER FW to slow changes in plasma equilibrium and energy, which during ITER operation will be monitored by the real-time wide angle infrared camera system. This allows computationally efficient runs for rapid controller performance assessment but can also assist operation scenario design by pre-empting potential heat load issues. FWHLC is designed to react to measured and predicted FW temperatures overcoming predesigned limits with real-time variations of the plasma shape or auxiliary power references, aiming to reduce thermal loads before a premature plasma termination is required to protect the FW. The new scheme is tested in closed loop simulations, where perturbations to nominal ITER low current scenarios are introduced in the wall thermal model to trigger the response of FWHLC, supporting the effectiveness of the proposed policy.

Structure and function of skin barrier lipids: Effects of hydration and natural moisturizers in vitro

Lipid membranes play a crucial role in regulating the body’s water balance by adjusting their properties in response to hydration. The intercellular lipid matrix of the stratum corneum (SC), the outermost skin layer, serves as the body’s primary defense against environmental factors. Osmolytes, including urocanic acid (UCA) and glycerol, are key components of the natural moisturizing factor that help the SC resist osmotic stress from dry environments. This study examines the effects of UCA and glycerol (each at 5 mol %) on isolated human SC lipids. For this, different techniques were employed, offering complementary information of the system’s multiscale characteristics, including humidity-scanning quartz crystal microbalance with dissipation monitoring, infrared spectroscopy, x-ray diffraction, electrical impedance spectroscopy, and studies of water loss and permeability. Our results show that UCA increases water sorption and makes lipid films more liquid-like at high relative humidity, without significantly altering the lipid lamellar structure, chain order, or orthorhombic chain packing. Lipid films containing UCA exhibited higher water loss and significantly higher model drug permeability compared to lipid films without UCA. Further, incorporation of UCA resulted in kinetically faster changes in electrical properties upon contact with aqueous solution compared with control lipids. These observations suggest that UCA reduces lipid cohesion in regions other than the acyl chain-rich leaflets, which may impact SC desquamation. In contrast, glycerol did not influence the hydration or permeability of the SC lipid matrix. However, it increased the proportion of orthorhombic domains at high humidities and slowed the kinetics of the hydration process, as evidenced by slower changes in the dielectric properties of the lipid film. These findings suggest that glycerol enhances lipid cohesion rather than increasing water uptake, which is typically the expected function of humectants. Consequently, UCA and glycerol appear to have distinct roles in maintaining epidermal homeostasis.

The Fertility Transition in Sub-Saharan Africa: The Role of Structural Change.

Despite recent economic growth and reductions in child mortality in many African countries, the region has experienced a slow fertility transition. In this study, we explore whether the slow structural economic change on the continent can explain this discrepancy. We construct a unique panel dataset combining Demographic and Health Surveys and nighttime light intensity data (an indicator of industrialization) from 57 countries at the subnational region level over three decades to analyze the drivers of fertility transitions across low- and middle-income countries. Our results confirm that household wealth, reduced child mortality, and female primary education are crucial for fertility declines. However, our analysis also highlights the importance of indicators of structural economic change, including the share of labor in nonagricultural occupations, industrialization, the share of women with higher education, and the formalization of the economy. Our simulations suggest that if high-fertility countries in sub-Saharan Africa underwent structural economic transformations comparable to those of other low- and middle-income countries with low fertility rates, their fertility levels could fall by 1 to 1.6 children.

Climate gradient‐driven intraspecific aggregation propensity linked to interpatch modulation in grassland communities

AbstractThe response of vegetation to climate change on a large scale should be studied at the community level rather than the species level. This necessitates a focused exploration of emerging spatial patterns. Here, we surveyed 264 sites in the Inner Mongolia typical steppe, using the “needling” method to investigate 39,600 clumps formed through the coexistence relationships of dominant species. We found that the effects of slow climate change on grassland communities can be categorized into two general trends: (1) a monotone relationship, characterized by changes in the number of dominant species, compositional diversity, and optimal patch area, and (2) a unimodal relationship, reflected in variations in the number of patches and interspecific associations. The two distinct trends, connected by optimal patch area, concurrently support both the habitat amount hypothesis and the intermediate disturbance hypothesis. These findings suggest that climate change indirectly influences the area and amount of vegetation patches by regulating the arrangement of clumps. Moreover, they indicate that it is the distribution, rather than the number, of species that serves as the front line for plant communities adapting to climate change.

Novel and reappraised wide-band EEG findings in migraineurs: its correlation with several clinical variables

ObjectiveCortical spreading depolarization is one possible pathogenesis of migraine, of which slow neurophysiological change is barely recorded in conventional EEG settings. Using wide-band EEG conditions, we reappraised the features of EEG in migraineurs, including subdelta-band EEG changes. MethodsThis retrospective study included 144 patients with migraine. We delineated EEG of focal delta slow (FDS) (1-4 Hz) by time constant (TC) 0.3 s and focal subdelta slow (FSDS) (< 1 Hz) by TC 2 s. Relationships between clinical variables and EEG findings were evaluated. ResultsOf 144 patients, 39 had aura and 105 did not. FSDS and FDS were observed in 38 and 58 patients, respectively. No EEG was recorded during the aura. In multivariate analysis with the phase of migraine, family history, age, and percentage of sleep during EEG recording, the phase of migraine was related to the occurrence of FSDS (postdrome vs interictal, prodrome, and headache respectively (OR = 49.00 [95% CI = 3.89-616.66], 46.28 [2.99-715.78], 32.79 [2.23-481.96], p = 0.0026, 0.0061, 0.011). FDS was clinically unremarkable for differential evaluation. ConclusionsWide-band EEG abnormality in migraineurs, i.e., FSDS, can be affected by migraine phase. SignificanceWide-band EEG finding could be a biomarker related to clinical variables in migraines.

De-noising magnetotelluric data based on machine learning

The magnetotelluric (MT) sounding is a common geophysical exploration technique, but it is highly polluted by various types of cultural noise. In the realm of MT data processing, traditional techniques often rely on the quality of the measured MT data. Conventional MT time domain denoising methods tend to eliminate valuable signals, potentially leading to unreliable resistivity estimates. To address this concern, we propose employing machine learning to effectively suppress strong noise interference in MT data, thereby preventing the loss of valuable signals. We augment this approach with mathematical morphological filtering (MMF) to capture low-frequency signals, preserving their integrity. We constructed a signal sample library based on a substantial volume of signal samples. Through consistent training, we establish a support vector machine (SVM) classification model that distinguishes high-quality signal fragments from noisy signals. Subsequently, we use adaptive K-singular value decomposition (K-SVD) dictionary learning to extract noise profiles and suppress noisy signals. To validate the feasibility of our method, we apply machine learning to measured data from two distinct observation areas. The measured data were analyzed and processed, and the results were compared with the robust results. This method can effectively eliminate large-scale strong interference in time domain sequences and preserve more low-frequency slow change information and high-quality signals in the reconstructed signals. The apparent resistivity phase curve of synthetic data is smoother and more continuous, and the data quality in the low-frequency range is significantly improved. The results can more accurately and reliably reflect underground electrical structure information.

Intelligent full-stage stable fault diagnosis method for subsea production system

Subsea production system is a large slowly changing system. Through fault diagnosis, the state of it can be got in time, and this can ensure the smooth production of oil and gas. When the training data is insufficient, the performance of data driven methods is poor. Influenced by slow changes, the performance of model-based fault diagnosis methods may fluctuate. For long-term stable monitoring of subsea production system, an intelligent full-stage stable fault diagnosis method for subsea production system is proposed. A fault diagnosis framework with both digital and model drives is proposed. Model-based diagnostic model is used to achieve accurate diagnosis under insufficient data. Data driven model is trained continuously to achieve stable diagnosis. A dynamic digital twin model is proposed to accelerate the process of training. A case from the South China Sea is used to study the performance of this method. The results show that this method is stable over a long period.

Exploring the relationship between climate change events and migration decisions: Evidence from a choice experiment in Bangladesh

Bangladesh is a low-lying deltaic nation with high vulnerability to climate-related hazards. Hatiya Island is located in Southern Bangladesh, and residents rely significantly on fishing and agriculture for their livelihoods. However, the island's primary production systems face a threat from intense and frequent sudden onset environmental hazards such as river erosion, cyclones, and flooding, as well as slow onset environmental hazards like saline intrusion, tidal inundation, and rising temperatures. Little is known about the effect sudden onset and slow onset hazards have on the migration patterns of Hatiya's residents. The aim of this study is to investigate the relationship between climatic events and the migration decisions of residents on Hatiya Island. To explore this link, we collected empirical data from 337 respondents on Hatiya Island using a choice experiment survey. The five attributes were used to construct migration scenarios, including climate events at Hatiya Island, the distance of migration, the migration type, the social network at the potential destination, and the difference in income between Hatiya Island and the potential destination. The findings from the mixed logit model indicated that climate events at Hatiya, migration distance, and income gap affected residents' migration decisions. Extreme slow onset change and extreme sudden onset change were less likely to induce migration compared to the moderate level of slow and sudden onset change. However, residents were more likely to migrate as the income difference between Hatiya and the prospective destination increased. Residents were willing to migrate at a 9.4 % increase in income for extreme sudden onset change, while residents required a 14.19 % increase in income to migrate in response to extreme slow onset change. As such, policies are needed to support migrants in receiving areas through improved social protection systems, to support migrant-sending households' transformative adaptation in origin areas, and to bolster community-driven adaptation initiatives in Hatiya. The findings from this study can contribute to the existing literature on the relationship between environmental hazards and mobility and inform policymakers on how to design policies that support vulnerable populations.

Serial dependence as a mechanism involved in slow change blindness

Serial dependence as a mechanism involved in slow change blindness

Chechen Diaspora in the countries of the Middle East: on the issue of generational continuity and socio-political dynamics of communities

The article is concerned with little-studied dynamics of the Chechen and, more broadly, North Caucasian Diaspora in Jordan, Syria and Turkey, starting with the emergence of these communities in the 19th century during the era of the Ottoman Empire. The article traces a slow change in the character of these diasporic communities from the “exodus” from the Russian Empire to the building up of international relations with the Chechen Republic in the 21st century on the basis of “cultural similarity” and the importance of the idea of an ideal ancestral home. The article argues that the Diaspora changed its character from a refugee Diaspora to an imperial Diaspora during the Ottoman Empire, and then became a “cultural Diaspora” with predominant narratives of cultural and geographical proximity with the country of origin a century after the resettlement. The article also argues that cultural and geographical similarity leads to the importance of ethnopolitical factor in international relations and diasporic politics.