Dynamical Variables Research Articles

Dark Carbon Fixation Measurements in the East Sea (Sea of Japan)

The vertical distribution patterns of daily primary production and dark carbon fixation were investigated at three stations in the East/Japan Sea (hereafter East Sea), a semi-enclosed marginal sea in the northwest Pacific Ocean. Our results displayed consistent vertical patterns of daily primary production at two of the stations, while the third station exhibited a markedly different distribution pattern, highlighting localized variations in production dynamics. In contrast, dark carbon fixations displaying varying vertical patterns among the stations are not specific enough to have much meaning. Water column-integral values showed differences in the contribution of dark carbon fixation to total primary production, ranging from 4.5% to 27.1%. These variations may reflect environmental parameters such as nutrient concentrations. However, our study is limited by the lack of direct data on the microbial community structure and chemoautotrophic activities, which are crucial for a more comprehensive understanding of these patterns. Understanding the environmental drivers of dark carbon fixation is crucial for elucidating carbon cycling dynamics in the East Sea. Notably, dark carbon fixation could contribute up to one-third of primary production in the region as an additional source of newly produced organic matter, highlighting the need for further investigation into this previously overlooked process.

The construction of brand positioning: an exploratory study on Nescafé Origens do Brasil

The article is a study on the positioning of the Nescafé Origens do Brasil brand, built with a focus on the place of origin of the inputs used in its products. We sought to discuss the marketing strategies adopted by the brand, Nestlé's sustainability flagship in Brazil, and the behavior of the Brazilian coffee consumer. The research is qualitative in nature and has a methodological procedure that involves bibliographical research on marketing, with a theoretical approach to brand positioning. Martin-Barbero's (2001) concept of mediations is also adopted to explore the communication process present in the advertising film “The Carbon Neutral Journey”, by Nescafé Origens do Brasil. The aim is to understand how the variables of the brand's communication dynamics contribute to the construction of its strategic positioning. As a result, it was identified that the mechanisms adopted by the brand contributed to (I) the construction of a strategic positioning based on sustainable development, (II) overvaluing the products; (III) create a competitive advantage; (IV) feed it back based on information from consumers, who begin to give new meaning to the choice of coffee they consume.

Control strategies for emerging infectious diseases: Crimean-Congo hemorrhagic fever management.

Crimean-Congo Hemorrhagic Fever (CCHF) is a significant public health concern transmitted by ticks. This study seeks to thoroughly grasp the epidemiology and transmission patterns of CCHF, which is caused by the CCHF virus (CCHFV), a member of the Nairovirus genus in the Bunyaviridae family. The study investigates the global distribution and endemicity of CCHF, its mortality rates, modes of transmission (including tick bites, contact with infected animal blood, and limited person-to-person transmission), and factors influencing its prevalence across different regions. Genetic diversity within CCHFV and its impact on transmission dynamics are explored, along with efforts to control the disease through tick prevention, antiviral treatment, and the development of vaccines and diagnostics. CCHFV exhibits widespread distribution, particularly in the Middle East, Africa, Asia, and Eastern Europe, with an overall mortality rate of approximately 30% and a case fatality rate ranging from 10% to 40%. Transmission occurs primarily through tick bites and contact with infected animal blood, with limited person-to-person transmission. Livestock workers, slaughterhouse employees, and animal herders in endemic areas are most affected by their frequent interaction with sick animals and ticks. Genetic diversity within CCHFV contributes to variations in transmission dynamics, complicating control efforts. Antiviral ribavirin shows efficacy in treating CCHF infection. This study underscores the importance of further research to understand the enzootic environment, transmission routes, and genetic diversity of CCHFV for effective control measures, including the development of vaccines, treatment options, and diagnostics.

Analyzing the FMN-heme interdomain docking interactions in neuronal and inducible NOS isoforms by pulsed EPR experiments and conformational distribution modeling.

Nitric oxide synthases (NOSs), a family offlavo-hemoproteins with relatively rigid domains linked by flexible regions, require optimal FMN domain docking to the heme domain for efficient interdomain electron transfer (IET). To probe the FMN-heme interdomain docking, the magnetic dipole interactions between the FMN semiquinone radical (FMNH•) and the low-spin ferric heme centers in oxygenase/FMN (oxyFMN) constructs of neuronal and inducible NOS (nNOS and iNOS, respectively) were measured using the relaxation-induced dipolar modulation enhancement (RIDME) technique. The FMNH•RIDME data were analyzed using the mesoscale Monte Carlo calculations of conformational distributions of NOS, which were improved to account for the native degrees of freedom of the amino acid residues constituting the flexible interdomain tethers. This combined computational and experimentalanalysis allowed for the estimation of the stabilization energies and populations of the docking complexes of calmodulin (CaM) and the FMN domain with the heme domain. Moreover, combining the five-pulse and scaled four-pulse RIDME data into a single trace has significantly reduced the uncertainty in the estimated docking probabilities. The obtained FMN-heme domain docking energies for nNOS and iNOS were similar (-3.8kcal/mol), in agreement with the high degree of conservation of the FMN-heme domain docking interface between the NOS isoforms. In spite of the similar energetics, the FMN-heme domain docking probabilities in nNOS and iNOS oxyFMN were noticeably different (~ 0.19 and 0.23, respectively), likely due to differences in the lengths of the FMN-heme interdomain tethers and the docking interface topographies. The analysis based on the IET theory and RIDME experiments indicates that the variations in conformational dynamics may account for half of the difference in the FMN-heme IET rates between the two NOS isoforms.

Ebullition drives high methane emissions from a eutrophic coastal basin

The production of methane in coastal sediments and its release to the water column is intensified by anthropogenic eutrophication and bottom water hypoxia, and it is still uncertain whether methane emissions to the atmosphere will be enhanced. Here, we assess seasonal variations in methane dynamics in a eutrophic, seasonally euxinic coastal basin (Scharendijke, Lake Grevelingen). In-situ benthic chamber incubations reveal high rates of methane release from the sediment to the water column (74–163 mmol m−2 d−1) during monthly measurements between March and October 2021. Comparison of these in-situ total benthic methane fluxes and calculated diffusive fluxes point towards a major role for ebullition. In spring and fall, when the water column was oxic, microbial removal of dissolved methane occurred aerobically in the bottom water. In summer, in contrast, dissolved methane accumulated to concentrations of up to 67 μmol L−1 below the oxycline. Shifts in δ13C–CH4 and δD-CH4 towards higher values and the abundant presence of methane oxidizing bacteria point towards removal of methane around the oxycline, likely through both aerobic and anaerobic pathways, with the latter possibly linked to iron oxide reduction. Shifts in δ13C–CH4 and δD-CH4 to lower values above the oxycline indicate that bubble dissolution contributed to dissolved methane. Methane emissions to the atmosphere were observed in all seasons with the highest, in-situ measured diffusive fluxes (1.2 mmol m−2 d−1) upon the onset of water column mixing at the end of summer. Methane release events during the measurement of in-situ water-air fluxes and model calculations point towards a flux of methane to the atmosphere in the form of bubbles, which bypass the microbial methane filter. The model calculations suggest a potential year-round ebullitive methane flux between 30 and 120 mmol m−2 d−1. We conclude that methane emissions from eutrophic coastal systems may be much higher than previously thought because of ebullition.

Distributed Adaptive Output Feedback Consensus for Nonlinear Stochastic Multiagent Systems by Reference Generator Approach.

This article investigates the distributed leader-following consensus for a class of nonlinear stochastic multiagent systems (MASs) under directed communication topology. In order to estimate unmeasured system states, a dynamic gain filter is designed for each control input with reduced filtering variables. Then, a novel reference generator is proposed, which plays a key role in relaxing the restriction on communication topology. Based on the reference generators and filters, a distributed output feedback consensus protocol is proposed by a recursive control design approach, which incorporates adaptive radial basis function (RBF) neural networks to approximate the unknown parameters and functions. Compared with existing works on stochastic MASs, the proposed approach can significantly reduce the number of dynamic variables in filters. Furthermore, the agents considered in this article are quite general with multiple uncertain/unmatched inputs and stochastic disturbance. Finally, a simulation example is given to demonstrate the effectiveness of our results.

Seasonal Patterns and Abiotic Influences on Fall Armyworm Dynamics in Chhattisgarh's Maize Agroecology

The fall armyworm Spodoptera frugiperda (J. E. Smith), an invasive pest in India, exhibits variations in population dynamics and incidence levels compared to its native regions. Understanding these variations is crucial for developing effective management strategies. Given India's diverse agroecosystems and climatic conditions, continuous watch on pest dynamics is essential. In this context a fixed plot experiment for comprehensive analysis of fall armyworm population dynamics, incidence patterns across seasons, and damage potentials in the maize crop ecosystem of the Chhattisgarh plains, India was conducted across three seasons of the year 2022-23. The year-round activity of the pest and its potential to cause huge losses in the early stages of maize was reported. Specifically, the study provided insights into the comparative incidence of fall armyworm in the different maize growing seasons of the area with a maximum incidence in the rainy season (0.58 larvae/plant) followed by winter (0.56 larvae/plant) and summer (0.53 larvae/plant) seasons. Moreover, the incidence trends of fall armyworm in different growing stages of maize were analysed by considering egg laying pattern, leaf damage, larval incidence and plant damage parameters, which highlighted the plant age-dependent response of fall armyworm. The impact of various weather parameters in shaping the damage and population fluctuations was analysed, where the influence of temperature was significant. Results obtained in this study are crucial in devising ETL levels and developing integrated pest management strategies against fall armyworm.

Recovery of synchronized oscillations on multiplex networks by tuning dynamical time scales

The heterogeneity among interacting dynamical systems or variations in the pattern of their interactions occur naturally in many real complex systems. Often they lead to partially synchronized states like chimeras or oscillation suppressed states like in-homogeneous or homogeneous steady states. In such cases, it is a challenge to get synchronized oscillations in spite of prevailing heterogeneity. In this study, we present a formalism for controlling multi layer, multi timescale systems and show how synchronized oscillations can be restored by tuning the dynamical time scales between the layers. Specifically, we use the model of a multiplex network, where the first layer of coupled oscillators is multiplexed with an environment layer, that can generate various types of chimera states and suppressed states. We show that by tuning the time scale mismatch between the layers, we can revive the synchronized oscillations. We analyse the nature of the transition of the system to synchronization from various dynamical states and the role of time scale mismatch and strength of inter layer coupling in this scenario. We also consider a three-layer multiplex system, where two system layers interact with the common environment layer. In this case, we observe anti synchronization and in-homogeneous steady states on the system layers and by tuning their time scale difference with the environment layer, they undergo transition to synchronized oscillations.

Investigating Caregiving Dynamics and Subjective Cognitive Decline: An Analysis Using BRFSS Database Insights

Background: Subjective cognitive decline (SCD), characterized by self-reported memory or cognitive difficulties, is an increasing concern among older adults. This study utilizes data from the Behavioral Risk Factor Surveillance System (BRFSS) to examine SCD prevalence, professional consultation patterns, and caregiving dynamics among U.S. adults aged 45 years and older. Objectives: To explore trends in the prevalence of SCD, the extent of professional consultation among those experiencing SCD, and recent caregiving activities. The study aims to identify demographic variations and assess changes over time. Methods: This cross-sectional study analyzes BRFSS data from 2020 and 2022, which includes self-reported data on cognitive symptoms, professional consultation, and caregiving activities. Descriptive statistics and age-adjusted prevalence estimates were computed for SCD, professional consultation, and caregiving activities. Comparative analyses were performed to identify trends between 2020 and 2022, and to assess variations across demographic groups. Results: Between 2020 and 2022, the age-adjusted prevalence of SCD among adults aged 45 and older rose from 9.7% to 11.7%, affecting both genders. Prevalence increased from 9.4% to 11.8% for males and from 10.0% to 11.7% for females. In 2022, Black non-Hispanic adults had a prevalence of 12.2%, Hispanic adults 12.3%, Asian non-Hispanic adults 9.4%, Hawaiian or Pacific Islander non-Hispanic adults 9.9%, American Indian or Alaska Native non-Hispanic adults 21.1%, and Multiracial non-Hispanic adults 19.2%. Professional consultation rates declined from 47.7% to 44.3%. Caregiving for individuals with dementia or cognitive impairment was reported by 22.4% in 2020, decreasing slightly to 21.3% in 2022. General caregiving showed a slight increase from 18.9% to 19.5%. Variations in SCD prevalence and caregiving activities were observed across gender, race, and ethnicity. Conclusion: The study highlights a rising trend in SCD and variations in professional consultation and caregiving dynamics. These findings emphasize the need for targeted public health strategies to address cognitive decline and support caregivers. Enhancing access to healthcare and caregiver resources is crucial for improving outcomes for affected individuals and their caregivers.

Modelling the effectiveness of an isolation strategy for managing mpox outbreaks with variable infectiousness profiles

The global outbreak of mpox in 2022 and subsequent sporadic outbreaks in 2023 highlighted the importance of nonpharmaceutical interventions such as case isolation. Individual variations in viral shedding dynamics may lead to either premature ending of isolation for infectious individuals, or unnecessarily prolonged isolation for those who are no longer infectious. Here, we developed a modeling framework to characterize heterogeneous mpox infectiousness profiles – specifically, when infected individuals cease to be infectious – based on viral load data. We examined the potential effectiveness of three different isolation rules: a symptom-based rule (the current guideline in many countries) and rules permitting individuals to stop isolating after either a fixed duration or following tests that indicate that they are no longer likely to be infectious. Our analysis suggests that the duration of viral shedding ranges from 23 to 50 days between individuals. The risk of infected individuals ending isolation too early was estimated to be 8.8% (95% CI: 6.7–10.5) after symptom clearance and 5.4% (95% CI: 4.1–6.7) after 3 weeks of isolation. While these results suggest that the current standard practice for ending isolation is effective, we found that unnecessary isolation following the infectious period could be reduced by adopting a testing-based rule.

Size resolved particle contribution to vehicle induced ultrafine particle number concentration in a metropolitan curbside region

The concentrations and behavior of nano particles (10–1000 nm) in Delhi, a densely populated megacity, in different seasons (winter, spring, summer, monsoon, and autumn) are examined, for the first time. The concentration of particles is classified into four different sizes as Nnuc (10–30 nm, nucleation), Nsatk (30–50 nm, small Aitken), Nlatk (50–100 nm, large Aitken), and Nacc (100–1000 nm, accumulation mode), and the total (10–1000 nm) particle number concentration (PNC) as Ntotal. PNC ranges between 104 cm−3 and 106 cm−3 over Delhi during the year, and the highest concentration occurs in winter. Winter concentration is 2, 1.6 and 1.3 times higher than monsoon, summer, autumn and spring concentrations, respectively. Nnuc, Nsatk, Nlatk and Nacc and their respective contributions to total PNC exhibit significant seasonal variations. During winter Nlatk and Nacc contribute more to total due to coagulation, with Nacc alone contributing >40% to total PNC. Nnuc, Nsatk, and Nlatk are higher in spring and summer during mid-day due to nucleation and/or ultrafine particle burst events. The direct primary emissions from engine exhaust produce a prominent double hump structure during morning and evening peak hours in winter and autumn. PNC and their contributions exhibit day-night variations as they are influenced by variations in emission sources and meteorological parameters (wind speed, relative humidity, temperature, solar radiation and boundary layer height) between day and night. Carbon monoxide correlates positively with Nacc in all seasons (R2 ≥ 0.5) as fossil fuel emission is a predominant source for gases and particles in the study environment. These quantitative results on seasonal variations of nano particles and air pollutants together with the knowledge on seasonal variations in meteorological parameters and atmospheric dynamics provide a foundation which can positively contribute better to the urban planning and devising mitigation measures aimed at improving air quality and public health.

Integrated nexus approach to assessing climate change impacts on grassland ecosystem dynamics: A case study of the grasslands in Tanzania

This study addresses the intricate interplay between climate, vegetation, and livestock dynamics in Tanzania within the Climate-Vegetation-Livestock (CVL) nexus through a quantitative assessment. By examining the temporal and spatial relationships between vegetation indices (NDVI, EVI, NPP) and key climatic variables (Precipitation, Temperature, Evapotranspiration) from 2009 to 2019, and projecting to 2050, this research aims to elucidate vegetation responses to climate change and its subsequent impacts on livestock. To this end, the relationship between the vegetation dynamics indicators (NDVI, NPP) and climate parameters is evaluated to quantify the vegetation response to climate change using statistical models. Next, an examination of multicollinearity is conducted to investigate potential interactions (nexus) between variables, incorporating the correlation among independent variables. Notably, the evaluation of performance and accuracy for the mentioned models is conducted through the cross-validation method and validation indices. Ultimately, the variation between projected NPP and NDVI (average for 2040–2060) and the present NPP and NDVI (average for 2009–2020) identifies the regions that are most likely susceptible, showcasing the vegetation cover's reaction to climate change in different emission scenarios. The results unveil significant spatio-temporal variations in vegetation dynamics influenced by climatic factors, where higher precipitation and temperatures correlate with increased vegetation health and productivity. The projected fluctuations in NDVI and NPP values indicate varying trends across different regions, with a general decrease in vegetation density and productivity from the northeast to the west under both RCP2.6 and RCP8.5 scenarios by 2050. This decline is attributed to anticipated changes in precipitation and temperature patterns driven by climate change. Furthermore, significant declines in vegetation density and productivity under emission scenarios, particularly in the southern regions compared to the present, suggest greater vulnerability to climate change impacts. This highlights the need for targeted mitigation strategies in these vulnerable areas. Meanwhile, northeast areas under both NDVI and NPP will remain unchanged across both climate scenarios. Moreover, analysis of livestock distribution maps indicates areas of vulnerability under climate change scenarios, with implications for future livestock management and agricultural practices. These findings underscore the importance of proactive planning and targeted interventions to enhance resilience and sustainable development in vulnerable regions, emphasizing the need for integrated approaches that consider the complex interactions between climate, vegetation, and livestock dynamics.

Understanding the intricacies of rainfall dynamics using entropy measures

ABSTRACT The present study quantifies the variability of rainfall dynamics across multiple time-periods and various entropy measures. Daily gridded rainfall from 1951 to 2020 was used to study Haryana, India, based on entropy and advanced statistical methods. The post-monsoon season had high variability for Sirsa and Palwal districts due to the variation occurring in October and December. This means these months are responsible for causing variations in the winter season. Similarly, during the monsoon season, low variability was found in Panchkula, Ambala, Yamunanagar, and some parts of Kurukshetra due to low variability in July. It was also observed that intra-variability in both rainfall amounts and rainy days increased with an increase in the time scale for all districts in Haryana. However, inconsistency on rainy days was more pronounced than rainfall amounts, which further means that the distribution of rainfall intensity was highly inconsistent in the study region. This study provides valuable insights into regional rainfall characteristics, and it assists local farmers in adapting to resilient crop varieties and efficient water-management practices, especially during crop harvesting and planting. Also, those in urban planning, disaster vigilance, and many others may consider this study to make districts resilient and adaptable.

Coccidioidomycosis seasonality in California: a longitudinal surveillance study of the climate determinants and spatiotemporal variability of seasonal dynamics, 2000–2021

Coccidioidomycosis, an emerging fungal disease in the western USA, exhibits seasonal patterns that are poorly understood, including periods of strong cyclicity, aseasonal intervals, and variation in seasonal timing that have been minimally characterized, and unexplained as to their causal factors. Coccidioidomycosis incidence has increased markedly in recent years, and our limited understanding of intra- and inter-annual seasonality has hindered the identification of important drivers of disease transmission, including climate conditions. In this study, we aim to characterize coccidioidomycosis seasonality in endemic regions of California and to estimate the relationship between drought conditions and coccidioidomycosis seasonal periodicity and timing. We analysed data on all reported incident cases of coccidioidomycosis in California from 2000 to 2021 to characterize seasonal patterns in incidence, and conducted wavelet analyses to assess the dominant periodicity, power, and timing of incidence for 17 counties with consistently high incidence rates. We assessed associations between seasonality parameters and measures of drought in California using a distributed lag nonlinear modelling framework. All counties exhibited annual cyclicity in incidence (i.e., a dominant wavelet periodicity of 12 months), but there was considerable heterogeneity in seasonal strength and timing across regions and years. On average, 12-month periodicity was most pronounced in the Southern San Joaquin Valley and Central Coast. Further, the annual seasonal cycles in the Southern San Joaquin Valley and the Southern Inland regions occurred earlier than those in coastal and northern counties, yet the timing of annual cycles became more aligned among counties by the end of the study period. Drought conditions were associated with a strong attenuation of the annual seasonal cycle, and seasonal peaks became more pronounced in the 1-2 years after a drought ended. We conclude that drought conditions do not increase the risk of coccidioidomycosis onset uniformly across the year, but instead promote increased risk concentrated within a specific calendar period (September to December). The findings have important implications for public health preparedness, and for how future shifts in seasonal climate patterns and extreme events may impact spatial and temporal coccidioidomycosis risk. National Institutes of Health.

Personalized whole-brain activity patterns predict human corticospinal tract activation in real-time.

Transcranial magnetic stimulation (TMS) interventions could feasibly treat stroke-related motor impairments, but their effects are highly variable. Brain state-dependent TMS approaches are a promising solution to this problem, but inter-individual variation in lesion location and oscillatory dynamics can make translating them to the poststroke brain challenging. Personalized brain state-dependent approaches specifically designed to address these challenges are therefore needed. As a first step towards this goal, we tested a novel machine learning-based EEG-TMS system that identifies personalized brain activity patterns reflecting strong and weak corticospinal tract (CST) output (strong and weak CST states) in healthy adults in real-time. Participants completed a single-session study that included the acquisition of a TMS-EEG-EMG training dataset, personalized classifier training, and real-time EEG-informed single pulse TMS during classifier-predicted personalized CST states. MEP amplitudes elicited in real-time during personalized strong CST states were significantly larger than those elicited during personalized weak and random CST states. MEP amplitudes elicited in real-time during personalized strong CST states were also significantly less variable than those elicited during personalized weak CST states. Personalized CST states lasted for ~1-2 seconds at a time and ~1 second elapsed between consecutive similar states. Individual participants exhibited unique differences in spectro-spatial EEG patterns between personalized strong and weak CST states. Our results show for the first time that personalized whole-brain EEG activity patterns predict CST activation in real-time in healthy humans. These findings represent a pivotal step towards using personalized brain state-dependent TMS interventions to promote poststroke CST function.

Configuration of the early-mid Cenozoic extensional arc volcanism of the North Patagonian and the Southern Central Andes (33–44°S)

The late Eocene to earliest Miocene volcanic arc of the Southern Central Andes (33–40°S) and North Patagonian Andes (40–44°S) exhibits distinctive features that shed light on the dynamics of subduction-related volcanic activity in extensional tectonic settings. This period is particularly significant in the Andes, as it records the opening of forearc, intra-arc, and retroarc extensional basins, accompanied by high-volume volcanism, preceding the middle-late Miocene final major uplift phase of the Andes. This extensional event occurred in two phases, associated with changes in the geometry and dynamics of the subduction system: from oblique convergence at low rates during the late Eocene-early Oligocene to an orthogonal convergence at high rates during the late Oligocene-early Miocene. This study presents a compilation of field, petrographic, geochemical, and isotopic data from volcanic sequences in the North Patagonian Andes and the Southern Central Andes, aiming to analyze petrogenetic processes at different stages of arc evolution.During the late Eocene to early Oligocene, subduction-related volcanism occurred in a wide area from 100 to 300 km from the trench. In this period, magmatic arc rocks exhibit intermediate composition and a relatively homogeneous geochemical signature, transitional between tholeiitic and calc-alkaline series, with a marked subduction-related signature. These magmas originated from depleted mantle sources with high slab-derived fluid and sediment contributions and limited crustal interactions. In contrast, the late Oligocene to earliest Miocene volcanic activity, at the peak of the extensional regime, displays significant geochemical differences along the Andes. In the Southern Central Andes (33–40°S), volcanism displays compositions similar to those of the previous stage with varying influence from the subducting slab. In some sectors, magmas register the climax of the extensional conditions, whereas the youngest volcanic rocks towards the north (33–34°S) present evidence of the onset of contractional tectonics, demonstrated by the increased interaction of the magmas with a progressively thickened crust. In the North Patagonian Andes (40–44°S), subaerial and subaqueous arc-related lava flows are interbedded with marine deposits, indicating a rapid subsidence regime. These rocks exhibit geochemical features transitional from arc to E-MORB and even OIB compositions, which suggest a genesis primarily dominated by decompression melting of a heterogeneous mantle source. Above all, the contrasting nature of late Eocene-early Miocene arc products in the Southern Central and Patagonian Andes highlights that no single process can explain the changes in composition, distribution, and evolution of arc magmatism; rather, it is a combination of factors, including variations in subduction zone configuration and mantle dynamics and composition.

NH2(CH3)2CuCl3 Organic-Inorganic Hybrid Perovskite: Consideration to Crystal Structure, Thermodynamics, and Structural Molecular Dynamics.

The crystal structure of NH2(CH3)2CuCl3, an organic-inorganic hybrid perovskite, undergoes a phase transition from triclinic to monoclinic at the phase transition temperature T C of 287 K. We investigated the temperature dependencies of NMR chemical shifts and spin-lattice relaxation time T 1ρ to gain insights into the structural geometry and molecular dynamics during the transition from phase II to phase I at high temperatures. Analysis of the 1H and 13C NMR chemical shifts of the cation revealed a continuous change in the surrounding structural geometry with temperature, without any anomalous changes around T C. The sudden decrease in T 1ρ values from low to high temperatures indicated a significant variation in proton and carbon dynamics at T C, arising from the slowing motion of molecular dynamics across the phase transition. The activation energies E a obtained from the temperature dependence of T 1ρ for 1H and 13C were larger in phase I than in phase II. This suggests that molecular motions in phase II exhibit a higher degree of freedom compared to those in phase I, where they are more constrained. These findings on NH2(CH3)2CuCl3 are presented to enhance its potential applications by elucidating the crystal configuration and structural molecular dynamics of ABX3 type compound.

Wavelet Collocation Method for HIV-1/HTLV-I Co-Infection Model Using Hermite Polynomial.

In this study, the dynamic behavior of fractional order co-infection model with human immunodeficiency virus type 1 (HIV-1) and human T-lymphotropic virus type I (HTLV-I) is analyzed using operational matrix of Hermite wavelet collocation method. Also, the uniqueness and existence of solutions are calculated based on the fixed point hypothesis. For the fractional order co-infection model, its positivity and boundedness are demonstrated. Furthermore, different types of Ulam-Hyres stability are also discussed. The numerical solution of the model are obtained by using the operational matrix of the Hermite wavelet approach. This scheme is used to solve the system of nonlinear equationsthat are very fruitful and easy to implement. Additionally, the stability analysis of the numerical scheme is explained. The mathematical model taken in this work incorporates the biological characteristics of both HIV-1 and HTLV-I. After that all the equilibrium points of the fractional order co-infection model are found and their existence conditions are explored with the help of the Caputo derivative. The global stability of all equilibrium points of this model are determined with the help of Lyapunov functions and the LaSalle invariance principle. Convergence analysis is also discussed. Hermite wavelet operational matrix methods are more accurate and convergent than other numerical methods. Lastly, variations in model dynamics are found when examining different fractional order values. These findings will be valuable to biologists in the treatment of HIV-1/HTLV-I.

Combined use of physically based hydrological model and empirical models to improve parameterisation of erosion processes in a flash flood prone catchment

AbstractThis study assesses the effectiveness of a distributed physically based hydrological model (MARINE) to investigate erosion estimation during flash floods compared with other widely used empirical models derived from the Universal Soil Loss Equation (USLE) like Revised Universal Soil Loss Equation (RUSLE) and Modified Universal Soil Loss Equation (MUSLE). It is carried out on a small catchment in south‐eastern France, the Claduègne catchment. To compare the erosion volumes simulated by the three models, MARINE, MUSLE and RUSLE, a sensitivity analysis on the model parameters is carried out. According to physics‐based simulations, flood events fall into two categories: those dominated by raindrop erosion and those dominated by shear stress erosion. The results show that the erosion simulated by the three methods are comparable, except for events dominated by raindrop erosion suggesting that further research is needed to improve raindrop erosion within MARINE. Simulations from the MARINE model provide access to the spatio‐temporal variability of erosion dynamics during the event and can also be used to produce erosion/deposition maps, which are useful for environmental decision‐makers and planners in identifying areas at risk from erosion and deposition hazards.

Understanding patient-derived tumor organoid growth through an integrated imaging and mathematical modeling framework.

Patient-derived tumor organoids (PDTOs) are novel cellular models that maintain the genetic, phenotypic and structural features of patient tumor tissue and are useful for studying tumorigenesis and drug response. When integrated with advanced 3D imaging and analysis techniques, PDTOs can be used to establish physiologically relevant high-throughput and high-content drug screening platforms that support the development of patient-specific treatment strategies. However, in order to effectively leverage high-throughput PDTO observations for clinical predictions, it is critical to establish a quantitative understanding of the basic properties and variability of organoid growth dynamics. In this work, we introduced an innovative workflow for analyzing and understanding PDTO growth dynamics, by integrating a high-throughput imaging deep learning platform with mathematical modeling, incorporating flexible growth laws and variable dormancy times. We applied the workflow to colon cancer organoids and demonstrated that organoid growth is well-described by the Gompertz model of growth. Our analysis showed significant intrapatient heterogeneity in PDTO growth dynamics, with the initial exponential growth rate of an organoid following a lognormal distribution within each dataset. The level of intrapatient heterogeneity varied between patients, as did organoid growth rates and dormancy times of single seeded cells. Our work contributes to an emerging understanding of the basic growth characteristics of PDTOs, and it highlights the heterogeneity in organoid growth both within and between patients. These results pave the way for further modeling efforts aimed at predicting treatment response dynamics and drug resistance timing.