Temporal Dynamics Research Articles

Navigating Decision Making with Generalized Temporal Intuitionistic Fuzzy Sets and Soft Sets

The study addresses the challenges posed by evolving data within generalized intuitionistic fuzzy sets. Traditional methods often fall short in handling such complexity. To overcome this, we introduce the concept of a generalized temporal intuitionistic fuzzy set, extending the traditional framework to incorporate temporal dynamics. Additionally, we define a generalized temporal intuitionistic fuzzy soft set, integrating temporal aspects into the soft set framework. Recognizing the need for advanced operations like union and intersection to merge opinions across different periods, we propose practical solutions for decision-making in this dynamic context. Two novel multi-criteria decision-making methods are introduced, specifically designed to address decision-making problems within generalized temporal intuitionistic fuzzy soft sets. We develop Scilab codes for these methods, enabling the creation of a multiple-input single-output system. This system is applied to complex real-life examples, involving numerous parameters, time moments, and decision-makers. This comprehensive approach aims to provide robust tools and methodologies for decision-making processes amidst the intricate dynamics of temporal data within the framework of generalized temporal intuitionistic fuzzy sets.

Ultrafast Modulation of a Nonlocal Semiconductor Metasurface under Spatially Selective Optical Pumping.

Time-varying optical metasurfaces have attracted significant research attention for their ability to dynamically modulate the spectral characteristics of light and achieve effects not possible in static systems. Nonlocal metasurfaces, with their spatially extended resonant modes, have the potential to further expand these capabilities by enabling spatially selective modulation. Here, we experimentally explore the application of spatially structured femtosecond laser pumping to modulate a leaky guided mode in a semiconductor metasurface. Using angle-resolved transient transmission spectroscopy, we study the dynamic response of the system across a range of wavelengths and angles of incidence, observing frequency and momentum conversion. When the pumping location is varied, this technique allows for reconstructing the temporal dynamics of the probe pulse propagating in the metasurface mode. Our results not only demonstrate a versatile toolkit for spatiotemporal light control but also provide fundamental insight into the excitation of spatially extended modes in nonlocal metasurfaces.

Impacts of soil type on the temporal dynamics of antibiotic resistance gene profiles following application of composted manure

Farmland application of composted manure is associated with a risk of dissemination of antibiotic resistance genes (ARGs) in agricultural soils. However, the impact of soil type on the temporal dynamics of ARGs in agricultural soil remains largely unclear. The aims of this study were to study the persistence of composted manure-derived ARGs in six soil types representative for Chinese agriculture and to explore the underlying environmental drivers of soil ARG profiles in a controlled greenhouse experiment. Temporal dynamics of manure-derived ARGs was strongly affected by soil type. High persistence of fertilizer-derived ARGs was evident in red soil, yellow soil and sierozem soil, while a rapid decrease to near pre-fertilization levels (low persistence) was observed in yellow-brown soil, black soil and brown earth soil. The distribution of ARGs was linked to soil properties such as soil texture, pH and concentrations of heavy metals. More complex co-occurrence networks of ARGs and bacteria in red soil, yellow soil, and sierozem soil suggested a higher dissemination potential, which was consistent with the significantly increased abundance of MGEs in these three types of soils. Our findings highlight the necessity for developing tailored fertilization strategies for different soil types to mitigate environmental dissemination of ARGs.

Transcriptomic and bioinformatic analysis of Histone H2A-genes in Lates calcarifer and Oreochromis niloticus upon Vibrio vulnificus challenge

Histone H2A-derived antimicrobial peptides (AMPs) have been recognized for their antimicrobial properties and role in host defense mechanisms. The present study investigated the transcriptional responses of two histone H2A-derived AMPs, Lc-Histone from Lates calcarifer and GIFT-Histone from Oreochromis niloticus, in response to Vibrio vulnificus infection. The genes encoding these peptides were amplified and sequenced from fingerling stage fishes, followed by in silico analysis and phylogenetic characterization using ClustalW. Computational methods were used to assess their functional attributes, and in vivo bacterial challenge assay was employed to evaluate the efficacy. Sequence alignment identified conserved histone motifs, supporting their evolutionary conservation. The antimicrobial peptide database predicted membrane interaction potential, suggesting antimicrobial activity, alongside biofilm-active properties and efficient expression in heterologous protein systems. Expression profiling of Histone H2A-derived AMP genes under Gram-negative bacterial stimulation revealed distinct temporal dynamics, indicative of their innate immune response involvement. Significant gene expression variations were observed between L. calcarifer and O. niloticus. These baseline findings enhance understanding of host defense mechanisms and bacterial interactions in aquaculture. The antimicrobial potential of histone H2A-derived AMPs highlights their significance as therapeutic agents and warrants further investigation into their mechanisms of action and optimal usage in aquaculture settings.

Temporal dynamics of immune cell patterns in bladder cancer patients receiving Bacillus Calmette-Guérin therapy.

Bacillus Calmette-Guérin (BCG) is capable of enhancing the infiltration of immune cells into the tumour. However the temporal dynamics of immune cell patterns in patients receiving BCG instillation remains unclear. Ninety-six patients who underwent intravesical BCG therapy, comprising 46 responders and 50 non-responders, were retrospectively enroled to explore the evolving immune landscape. This study involved a detailed examination of sequential samples collected before, during, and after BCG treatment to assess BCG's influence on the immune microenvironment, employing techniques such as immunohistochemistry, fluorescent multiplex immunohistochemistry, and mass spectrometry techniques. Our study found that initial BCG instillation leads to enhanced immune cell infiltration, correlating with treatment efficacy, with responders exhibiting more pronounced increases. Non-responders experience a rise in immune cell infiltration and PD-L1 expression during the first instillation, which returns to baseline after treatment. In non-responders, BCG re-challenge fail to further increase immune cell infiltration into the tumour or improve patient outcomes. Strikingly, proteomics data revealed that GBP1 expression was induced by BCG treatment in non-responders. Our findings demonstrated the induction of tumour PD-L1 expression by BCG in non-responders, and therefore provide insights for the combination of BCG and anti-PD1/anti-PD-L1 therapy.

An improved framework for mapping and assessment of dynamics in cropping pattern and crop calendar from NDVI time series across a heterogeneous agro-climatic region.

The absence of spatial and temporal cropping information in semi-arid regions poses a significant challenge in assessing the dynamics of agricultural systems at river basin scales. Satellite remote sensing provides qualitative and quantitative information to derive vegetation dynamics over extensive areas of inherent complexities due to limitations in the availability of field data and the diverse nature of agricultural cropping practices. Utilizing phenological information derived from MODIS Normalized Difference Vegetation Index (NDVI) time series data from 2003-2004 to 2021-2022, this study derives major crop types, and cropping calendar (sowing, maturity, and harvest dates) for each season and year at 250-m resolution. This study introduces an integrated function-fitting model based on the Fast Fourier Transform (FFT) and a Lagrangian 3-point derivative-based approach to extract phenological events from the NDVI time series automatically. The derived phenological information is used in a hybrid crop classification algorithm that combines a rule-based decision tree approach (using the phenological events/dates) and a random forest classifier (using the phenological metrics) to generate the classified crop map at a river basin scale for multiple seasons and years. The implemented approach successfully captured sowing and harvesting dates for every crop growing season over 19years, with an RMSE of 9days, as observed with the available field survey data from 2015 to 2021. The classification results from this hybrid approach demonstrate an overall 82% accuracy. The proposed method shows a substantial improvement in cropping area estimation with a 60% reduction in MAE and RMSE compared to the existing algorithm. From the long time series of derived seasonal crop data (19-year analysis period), the influence of monsoonal activities and shifts on the spatial and temporal dynamics of sowing time and cropping patterns are assessed for a large river basin, demonstrating the utility of this continuous crop calendar. Further, an extensive analysis of results highlights farmers' adaptive strategies in response to dry and wet years, providing foundational insights for future studies on assessing the impacts of climate change. Hence, the hybrid framework adopted in this study for deriving a continuous crop calendar holds immense relevance for parameterizing and utilizing such information for developing river basin scale hydrologic and crop growth models for water resources planning and assessment.

Health-related quality of life dynamics: modeling insights from immunotherapy.

Understanding health-related quality of life (HRQoL) dynamics is essential for assessing and improving treatment experiences; however, clinical and observational studies struggle to capture their full complexity. We use simulation modeling and the case of Chimeric Antigen Receptor T-cell therapy-a type of cancer immunotherapy that can prolong survival, but carries life-threatening risks-to study HRQoL dynamics. We developed an exploratory system dynamics model with mathematical equations and parameter values informed by literature and expert insights.We refined its feedback structure and evaluated its dynamic behavior through iterative interviews. Model simulated HRQoL from treatment approval through six months post-infusion. Two strategies-reducing the delay to infusion and enhancing social support-were incorporated into the model. To dynamically evaluate the effect of these strategies, we developed four metrics: post-treatment HRQoL decline, recovery time to pre-treatment HRQoL, post-treatment HRQoL peak, and durability of the peak. Model captures key interactions within HRQoL, providing a nuanced analysis of its continuous temporal dynamics, particularly physical well-being, psychological well-being, tumor burden, receipt and efficacy of treatment, side effects, and their management. Model analysis shows reducing infusion delays enhanced HRQoL across all four metrics. While enhanced social support improved the first three metrics for patients who received treatment, it did not change durability of the peak. Simulation modeling can help explore the effects of strategies on HRQoL while also demonstrating the dynamic interactions between its key components, offering a powerful tool to investigate aspects of HRQoL that are difficult to assess in real-world settings.

Individual-level metabolic connectivity from dynamic [18F]FDG PET reveals glioma-induced impairments in brain architecture and offers novel insights beyond the SUVR clinical standard.

This study evaluates the potential of within-individual Metabolic Connectivity (wi-MC), from dynamic [18F]FDG PET data, based on the Euclidean Similarity method. This approach leverages the biological information of the tracer's full temporal dynamics, enabling the direct extraction of individual metabolic connectomes. Specifically, the proposed framework, applied to glioma pathology, seeks to assess sensitivity to metabolic dysfunctions in the whole brain, while simultaneously providing further insights into the pathophysiological mechanisms regulating glioma progression. We designed an index (Distance from Healthy Group, DfHG) based on the alteration of wi-MC in each patient (n = 44) compared to a healthy reference (from 57 healthy controls), to individually quantify metabolic connectivity abnormalities, resulting in an Impairment Map highlighting significantly compromised areas. We then assessed whether our measure of metabolic network alteration is associated with well-established markers of disease severity (tumor grade and volume, with and without edema). Subsequently, we investigated disruptions in wi-MC homotopic connectivity, assessing both affected and seemingly healthy tissue to deepen the pathology's impact on neural communication. Finally, we compared network impairments with local metabolic alterations determined from SUVR, a validated diagnostic tool in clinical practice. Our framework revealed how gliomas cause extensive alterations in the topography of brain networks, even in structurally unaffected regions outside the lesion area, with a significant reduction in connectivity between contralateral homologous regions. High-grade gliomas have a stronger impact on brain networks, and edema plays a mediating role in global metabolic alterations. As compared to the conventional SUVR-based analysis, our approach offers a more holistic view of the disease burden in individual patients, providing interesting additional insights into glioma-related alterations. Considering our results, individual PET connectivity estimates could hold significant clinical value, potentially allowing the identification of new prognostic factors and personalized treatment in gliomas or other focal pathologies.

On Hysteresis In A Variable Pitch Fan Transitioning To Reverse Thrust Mode And Back

Abstract A novel hysteresis phenomenon during the transition to and back from the reverse thrust mode in a Variable Pitch Fan (VPF) is identified and characterised in this work. This is done by using a three-dimensional (3D) fully transient Unsteady Reynolds averaged Navier?Stokes (URANS) with the transitioning fan blade aerofoils simulated by an adaptation of the mesh displacement method. The VPF is modelled to be transitioning in a modern 40000 lbf geared high bypass ratio turbofan engine architecture at "Approach Idle" engine power setting in a typical twin-engine airframe with the flaps, slats, and spoilers set for an aircraft touchdown airspeed of 140 knots. The transition to reverse thrust mode involves flow starvation into the engine, formation of recirculation zones in the bypass duct and the establishment of the reverse stream, all of which occurs in the opposing presence of the free stream flow at aircraft touchdown velocity. The transition back to forward flow mode involves the gradual re-establishment of the free stream which is opposed by the presence of the reverse stream within the engine. It is quantified that in the transition to reverse thrust, the blockage develops with a larger time delay than the disappearance of the blockage during the transition back due to the interplay of the temporal dynamics of fan blade motion and flow field response. The hysteresis phenomena described in this work are critical in properly developing control schedules to adapt for potential bi-stable flow field development during the landing run.

Effects of Climate Change on Temperature Sensitivity of Vegetation Growth in Huang-Huai-Hai Plain: Spatial–Temporal Dynamics and Ecological Adaptability

This paper explores the optimal temperature change in normalized difference vegetation index (NDVI) growth in the Huang-Huai-Hai Plain under the background of climate change, aiming to better cope with the impact of global warming on vegetation growth. The temporal and spatial variation characteristics of the NDVI and temperature factors were analyzed by using the NDVI, average temperature (Tavg), maximum temperature (Tmax) and minimum temperature (Tmin) datasets from 1982 to 2020. Through the Buishand U test and sliding slope detection, 1998 was determined to be a year of abrupt climate change. Furthermore, SHAP important feature analysis, a generalized additive model, correlation analysis, and other methods were used to identify the trend in the optimum temperature of vegetation growth before and after climate change. The results showed that (1) from 1982 to 2020, Tavg, Tmax, Tmin, and the NDVI in Huang-Huai-Hai Plain showed a significant upward trend. At the same time, the spatial distribution of these indicators shows the distribution characteristics of high in the south and low in the north. (2) The NDVI was positively correlated with Tavg, Tmax, and Tmin, and the correlation with Tmin was most significant. (3) The most suitable Tavg, Tmax, and Tmin intervals for vegetation growth were 20~30 °C, 25~35 °C, and 16~25 °C, respectively. (4) The optimum temperature range of vegetation growth was expanded after climate change, and the change rates of the Tavg and Tmax lower limits reached 24% and 25%, respectively, under the best condition of vegetation growth. (5) After abrupt climate change, the temperature suitable for vegetation growth increased significantly in the northern part of the plain but decreased slightly in the southern part.

Road network disruptions during extreme flooding events and their impact on the access to emergency medical services: A spatiotemporal vulnerability analysis

The impact of extreme floods can be significant, affecting humans, the environment and any type of human-built infrastructure. A further consequence of flooding is restricted access to medical facilities, including constraints to access more dedicated emergency medical services (EMS). To date, there has been a lack of investigation into EMS accessibility dynamics during extreme floods. The objective of this study is to gain a deeper understanding of the spatiotemporal changes in accessibility-based vulnerability related to EMS during a representative extreme flood event, which was simulated over a period of six days. The study assesses the spatial accessibility of EMS centres to populations located within 15-, 30-, 45-, and 60-minute travel time thresholds in Canton Bern, Switzerland, and applies floating catchment area method. The dynamic aspects of EMS access vulnerability during extreme floods were assessed in two different ways. Firstly, the ratios between accessibility change and accessibility under normal conditions in 1 km grid cells were calculated at hourly moments, t. Subsequently, the resulting values were used to calculate the average vulnerability score. Secondly, percentage changes of affected populations were evaluated for different accessibility classes during the flood event and under all-time high static flood conditions. Varied spatial patterns of accessibility were generally observed with respect to the road network and population distributions in the hilly and mountainous landscape. Extending evaluations to consider temporal dynamics revealed a complex pattern of accessibility gains and losses in different regions of the study area, including those where no direct flood impacts occurred on the road network. The application of a static, all-time high flood condition resulted in an overestimation of accessibility limits to EMS centres. While this overestimation was not considered to be critical, the application of a spatiotemporal accessibility-based vulnerability analysis method to EMS is considered to be more holistic. Insights from this study can be used to evaluate the effectiveness of EMS risk management plans with respect to evolving extreme flood scenarios.

Spatial and temporal dynamics of the prevalence of resistance genes and gastrointestinal pathogens in stool samples of German deployment returnees

AbstractBackgroundThe exploratory study assessed trends in the abundance of CTX-M-type extended spectrum beta-lactamase (ESBL) and vancomycin-resistance genes vanA and vanB in the stool samples of German soldiers and police officers returning from predominantly tropical deployments next to the common diarrheagenic Escherichia (E.) coli pathovars enteropathogenic Escherichia coli (EPEC), enterotoxigenic E. coli (ETEC) and enteroaggregative E. coli (EAEC)) as well as rarely imported Vibrio spp. between 2006 and 2024.MethodsSurveillance was performed applying real-time polymerase chain reaction and results were stratified by World Health Organization region of deployment as well as by deployment period. For the latter, the study interval was divided into three pre-COVID-19-pandemic periods, the COVID-19-pandemic period and the post-COVID-19-pandemic period. Averaged prevalences were used as references.ResultsIn stool samples of 1817 deployed German soldiers and 117 police officers, averaged prevalences were 47.9% and 24.8% for the ESBL-type beta-lactamase blaCTX-M, 30.2% and 14.5% for vanB, 9.0% and 17.9% for EPEC, 3.4% and 12.8% for ETEC, 4.0% and 3.4% for EAEC as well as 2.0% and 3.4% for Vibrio spp., respectively. While resistance genes peaked during early deployments, maximum prevalences for enteropathogens were seen later.ConclusionsThe assessment suggested time- and region-dependence of the assessed parameters.

Predicting Shot Accuracy in Badminton Using Quiet Eye Metrics and Neural Networks

This paper presents a novel approach to predicting shot accuracy in badminton by analyzing Quiet Eye (QE) metrics such as QE duration, fixation points, and gaze dynamics. We develop a neural network model that combines visual data from eye-tracking devices with biomechanical data such as body posture and shuttlecock trajectory. Our model is designed to predict shot accuracy, providing insights into the role of QE in performance. The study involved 30 badminton players of varying skill levels from the Chinese Swimming Club in Singapore. Using a combination of eye-tracking technology and motion capture systems, we collected data on QE metrics and biomechanical factors during a series of badminton shots for a total of 750. Key results include: (1) The neural network model achieved 85% accuracy in predicting shot outcomes, demonstrating the potential of integrating QE metrics with biomechanical data. (2) QE duration and onset were identified as the most significant predictors of shot accuracy, followed by racket speed and wrist angle at impact. (3) Elite players exhibited significantly longer QE durations (M = 289.5 ms) compared to intermediate (M = 213.7 ms) and novice players (M = 168.3 ms). (4) A strong positive correlation (r = 0.72) was found between QE duration and shot accuracy across all skill levels. These findings have important implications for badminton training and performance evaluation. The study suggests that QE-based training programs could significantly enhance players’ shot accuracy. Furthermore, the predictive model developed in this study offers a framework for real-time performance analysis and personalized training regimens in badminton. By bridging cognitive neuroscience and sports performance through advanced data analytics, this research paves the way for more sophisticated, individualized training approaches in badminton and potentially other fast-paced sports. Future research directions include exploring the temporal dynamics of QE during matches and developing real-time feedback systems based on QE metrics.

Temporal and Spatial Dynamics of Anthracnose (Colletotrichum sublineolum) Disease on Selected Sorghum Genotypes at Assosa Zone, Western Ethiopia

Background Over 500 million people worldwide rely on sorghum as a main food crop. About 10% of the daily caloric intake of households in Ethiopia’s northwest and eastern regions, including Benishangul Gumuz, comes from sorghum. A hemi-biotrophic fungal pathogen called Colletotrichum sublineolum caused anthracnose disease is among the biotic constraints of sorghum production. Methods Ten selected sorghum genotypes were assessed for sorghum anthracnose severity and its temporal and spatial dynamics on field plots in the districts of Assosa and Bambasi. The performance of the chosen sorghum genotypes was assessed using the following metrics: AUDPC, disease progress rate, yield-related trait, sorghum grain yield, and mean severity index. Anthracnose severity was evaluated using a 1–5 disease rating scale and assessments conducted at seven consecutive time points. Results Ten genotypes of sorghum were examined in the districts of Assosa and Bambasi, with mean anthracnose severity index ranging from 60-77 PSI and 53-82 PSI, respectively. AUDPC varied from 351 to 470 % days and 316 to 499 % days at Assosa and Bambasi districts, respectively. Bambasi district achieved a larger grain yield than the Assosa district. Assosa-1 demonstrated a significant level of disease pressure, yet the current investigation found that this genotype is the highest performing genotype in both locations. Conclusions There is a considerable positive link between the severity of anthracnose and the weekly total rainfall and relative humidity. At both trial sites, Mersa-1 continuously produced higher grain yields and reduced disease levels. Breeders might utilize the Baco Striga sorghum genotype as a check line in a breeding effort to resist anthracnose disease because it shown a high vulnerability to the disease at both locations.

Reactive surveillance of deaths involving a fall among the elderly in France, 2015 to March 2023

Abstract In 2022, France launched an elderly anti-fall plan with a goal to reduce fatal falls by 20% within 5 years. In a reactive monitoring approach ensuring comparability over the period, our study aims to develop an algorithm to identify deaths involving a fall using free-text medical causes in death certificates. This will enable proactive surveillance and evaluate plan effectiveness upon completion. We devised an algorithm based on textual search of fall-related expressions in death certificates. Performance metrics were assessed by contrasting algorithm-identified deaths with those flagged by ICD-10 fall-related codes. We compared also temporal dynamics between the algorithms from 2015 to 2021. Analysis was performed by age group, gender and place of death. Additionally, our algorithm enabled estimation of fall-related death trends from 2015 to 2023. The free-text algorithm exhibited robust performance, with a sensitivity and PPV of 95% when compared to deaths coded with an ICD-10 fall-related underlying or associated cause. Temporal trends between both were highly correlated (correlation coefficient=0.99). Instead of the usual 10000 deaths for fall, we revealed about 13000 deaths linked to falls considering both primary and associated causes. Among these, approximately three-quarters were coded as the underlying cause of death in ICD-10 coding. Regarding trends, the results are preliminary and suggested an increase in mortality rates related to falls detected by the algorithm between 2015 and 2022. Our approach distinguishes falls from other competing causes that often relegate them to secondary status in the chain of morbid events leading to death. With free-text medical causes accessible within 3 to 4 months, monitoring deaths involving falls can be done at a detailed geographic level and in a more reactive way. Looking ahead, we must also consider incorporating the impact of epidemics and/or heat waves to accurately evaluate the effectiveness of the anti-fall plan. Key messages • Algorithm using free-text of death certificates identify deaths involving falls. • Our innovative approach isolates falls from other competing causes, elevating their significance in death processes.

Long COVID Prevalence and Factors Among ethnically Diverse UK Healthcare Workers

Abstract Background Long COVID (LC), characterised by persistent symptoms post-acute COVID-19 infection, presents a growing public health concern. Data on prevalence among healthcare workers (HCWs) are lacking. Our study aimed to establish the proportion of UK HCWs with symptoms lasting beyond five weeks and identify factors associated with LC in a diverse cohort. Methods We conducted a cross-sectional analysis from December 2020 to March 2021. The main outcome was LC in relation to self-reported ethnicity. Univariable and multivariable logistic regression identified associations. Odds ratios (ORs) and adjusted odds ratios (aORs) were calculated with 95% confidence intervals (95%CIs). The multivariate analysis adjusted for demographic information, job role, household factors, health status, and existing long-term conditions. Missing data were imputed via chained equations. Results From 11,513 HCWs, 2,331 (20.2%) reported COVID-19, with 525 (22.5%) experiencing LC. 80.0% of those reporting LC were female, compared to 74.3% without LC were female. Participants with LC (median age 46 [IQR 36-54)]) were slightly older than those without LC (median age 41 [IQR 31-52]). In multivariable analyses of those who reported having had COVID-19, HCWs in nursing/midwifery roles (aOR 1.76, 95% CI 1.26-2.46, p = 0.001) and allied health professions (aOR 1.42, 95% CI 1.05-1.93, p = 0.023) had higher odds of reporting LC compared to those in medical roles. Significant associations were also found with self-reported depression, anxiety, and asthma. There was no significant association between ethnicity and experiencing LC. Conclusions In this large ethnically diverse cohort study, there is a substantial burden of LC among UK HCWs. Further research and collaborative efforts are urgently needed to address these factors effectively, develop targeted public health interventions, and understand the temporal and longitudinal dynamics of the condition. Key messages • One in five UK healthcare workers reported long COVID, indicating critical needs for targeted public health interventions. • Our study highlights urgent public health responses to address long COVID among healthcare professionals.

Temporal dynamics of cardiac remodelling in pressure overload: the key role of visceral adipose tissue

Abstract Background Myocardial fibrosis is a prominent pathological feature of cardiac remodelling in pressure overload. The underlying mechanisms of cardiac remodelling are complex including mechanical, inflammatory and neurohormonal factors. We have recently uncovered a pathological crosstalk between visceral adipose tissue (VAT) and heart in biological aging characterized by profibrotic proteins in both the VAT secretome and the plasma contributing to myocardial interstitial fibrosis. We hypothesize that a model of pressure overload induced by transverse aortic constriction (TAC) is responsible for VAT remodelling and a VAT profibrotic secretome, which in turn worsens cardiac fibrosis and alters cardiac function. Purpose To explore the effect of pressure overload on VAT and identify a crosstalk between heart and VAT. Methods We divided wild-type male mice (5-month-old, n=10/group) into two groups: a visceral Lipectomy group and a Sham group two weeks before a moderate TAC (26G; i.e. gradient between left ventricle and aorta of 30mmHg) or a sham surgery. We sacrificed the mice at 1- and 8-week after TAC. We performed in vivo metabolic tests, echocardiography, and invasive hemodynamics, followed by ex vivo tissue analysis. We then analysed the time-course of the cardiac transcriptomic signature in TAC vs control and the impact of visceral lipectomy in both groups. Results Our results show that TAC induced not only cardiac remodelling affecting the structure and function of the heart, but also VAT fibrosis and inflammation together with activation of adrenergic receptors (Adrb3). VAT became a major source of profibrotic (TGFb and osteopontin) and proinflammatory (TNFa, IL6 and PAI1) factors as early as 1 week after TAC. Interestingly, lipectomy prevented cardiac remodelling and dysfunction. After 1 week of TAC, cardiac transcriptomic profile showed a similar upregulation of profibrotic and prohypertrophic genes in TAC groups in presence or absence of lipectomy. After 8 weeks of TAC, lipectomy prevented the upregulation of those genes and transcriptomic profile of TAC+lipectomy was similar to sham group (Figure). PCR analysis confirmed the lower expression of profibrotic (TGFb, Osteopontin), prohypertrophic (Nppa) and contractility genes (Adr1b) in TAC+Lipectomy vs TAC group after 8 weeks of TAC. Conclusion Our results suggest that VAT is detrimental to cardiac structure and function by releasing a profibrotic and a proinflammatory secretome that aggravates the cardiac remodelling induced by TAC. Conversely, lipectomy prevents the early development of TAC-induced cardiac fibrosis.

Spatial and Temporal Patterns of Grassland Species Diversity and Their Driving Factors in the Three Rivers Headwater Region of China from 2000 to 2021

Biodiversity loss will lead to a serious decline for ecosystem services, which will ultimately affect human well-being and survival. Monitoring the spatial and temporal dynamics of grassland biodiversity is essential for its conservation and sustainable development. This study integrated ground monitoring data, Landsat remote sensing, and environmental variables in the Three Rivers Headwater Region (TRHR) from 2000 to 2021. We established a reliable model for estimating grassland species diversity, analyzed the spatial and temporal patterns, trends of change, and the driving factors of changes in grassland species diversity over the past 22 years. Among models based on diverse variable selection and machine learning methods, the random forest (RF) combined stepwise regression (STEP) model was found to be the optimal model for estimating grassland species diversity in this study, which had an R2 of 0.44 and an RMSE of 2.56 n/m2 on the test set. The spatial distribution of species diversity showed a pattern of abundance in the southeast and scarcity in the northwest. Trend analysis revealed that species diversity was increasing in 80.46% of the area, whereas 16.59% of the area exhibited a decreasing trend. The analysis of driving factors indicated that the changes in species diversity were driven by both climate change and human activities over the past 22 years in the study area, of which temperature was the most significant driving factor. This study effectively monitors grassland species diversity on a large scale, thereby supporting biodiversity monitoring and grassland resource management.

Temporal Trends in Dementia Incidence in Sweden: Insights from NEAR Data

Abstract Background This study investigates the temporal trends of dementia incidence in Sweden over a span of 35 years, drawing on data from the National E-Infrastructure for Aging Research (NEAR). Methods Our comprehensive analysis encompassed over 10,000 participants aged 50 and above. We estimated dementia incidence rates across the study period, considering birth cohort, age, and sex while controlling for potential confounders. Additionally. We explored potential modifiers influencing the estimated trend of dementia incidence. Results The analysis reveals a notable decline in dementia incidence rates, particularly among individuals born in more recently. Octogenarians born in the 1920-1930 cohort had a 74% lower dementia incidence rate compared to their counterparts born before 1900. Similarly, nonagenarians born in the 1910-1920 cohort experienced a 45% lower incidence rate. We further investigate determinants influencing this declining trend, shedding light on various factors shaping the observed patterns. Conclusions The incidence rate of dementia in Sweden has shown a consistent decline over the past 35 years, with a notable birth cohort effect indicating lower rates among individuals born more recently. This finding suggests evolving factors in lifestyle, healthcare, or environmental conditions contributing to improved cognitive health outcomes. Key Messages • These findings underscore the importance of proactive public health measures aimed at sustaining and enhancing cognitive health outcomes among aging populations. • Understanding the temporal dynamics and underlying factors driving these trends can inform strategies for promoting healthy aging and improving the quality of life for older adults.

Accelerated Decline in Vegetation Resilience on the Tibetan Plateau

ABSTRACTThe ability of ecosystems to resist and recover from external disturbances is declining due to climate change, increased frequency of disasters, and intensified human activities. Global vegetation resilience is considered to be at risk of being lost. The sensitive and fragile Tibetan Plateau (TP) has experienced changes in climate and management patterns over the past five decades, but due to the complexity of defining resilience, there is still no unified understanding of the spatial patterns and long time‐series trends of resilience on the TP. In this study, we introduce the method based on critical slowing down, making it possible to clarify the spatial distribution and temporal dynamics of resilience on the TP. The results show low resilience on the northeastern and southwestern edges of the TP and in the Three River Source region. The area experiencing resilience loss is approximately 1.16–1.44 times larger than the area of gain. Vegetation resilience on the TP has exhibited a declining trend, with the rate of decline after 2014 being more than double that of the preceding period. Factors such as elevation, vegetation type, and hydrothermal condition significantly influence the spatial and temporal patterns of resilience. These findings improve our understanding of vegetation resilience on the TP and its ecosystem vulnerability. We also recommend that ecological restoration efforts be maintained and regularly assessed.