Long-term Scale Research Articles

Citrus supplementation in subjective cognitive decline: results of a 36-week, randomized, placebo-controlled trial

BackgroundDeveloping interventions for older adults with subjective cognitive decline (SCD) has the potential to prevent dementia in this at-risk group. Preclinical models indicate that Citrus-derived phytochemicals could benefit cognition and inflammatory processes, but results from clinical trials are still preliminary. The aim of this study is to determine the effects of long-term supplementation with Citrus peel extract on cognitive performance and inflammation in individuals with SCD.MethodsEighty participants were randomly assigned to active treatment (400 mg of Citrus peel extract containing 3.0 mg of naringenin and 0.1 mg of auraptene) or placebo at 1:1 ratio for 36 weeks. The primary endpoint was the change in the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) total score across the 36-week trial period. Other cognitive outcomes included tests and scales evaluating verbal memory, attention, executive and visuospatial functions, and memory concerns. The secondary endpoint was the change of interleukin-8 (IL-8) levels over the 36-week trial period in a subsample of 60 consecutive participants. An Intention-to-treat approach with generalized linear mixed models was used for data analysis.ResultsThe RBANS total score showed significant improvement in both Citrus peel extract and placebo groups at 36 weeks (p for time < .001, d = 0.36, p time x treatment = .910). Significant time effects were also found in cognitive domains of short- and long-term verbal memory (p < .001) and scales of subjective memory (p < .01), with no significant time x treatment interaction. The largest effect sizes were observed in verbal memory in the placebo group (d = 0.69 in short-term, and d = 0.78 in long-term verbal memory). Increased IL-8 levels were found at 36-week follow-up in both Citrus peel extract and placebo groups (p for time = .010, d = 0.21, p time x treatment = .772). Adverse events were balanced between groups.ConclusionsIn this randomized clinical trial, long-term Citrus peel extract supplementation did not show cognitive benefits over placebo in participants with SCD, possibly due to high placebo response. These findings might have specific implications for designing future nutraceutical trials in individuals experiencing SCD.Trial registrationThe trial has been registered at the United States National Library of Medicine at the National Institutes of Health Registry of Clinical Trials under the code NCT04744922 on February 9th, 2021 (https://www.clinicaltrials.gov/ct2/show/NCT04744922).

Runoff Change Characteristics and Response to Climate Variability and Human Activities Under a Typical Basin of Natural Tropical Rainforest Converted to Monoculture Rubber Plantations

Climate variability and human activities are major influences on the hydrological cycle. However, the driving characteristics of hydrological cycle changes and the potential impact on runoff in areas where natural forests have been converted to rubber plantations on a long-term scale remain unclear. Based on this, the Mann–Kendall (MK) and Pettitt breakpoint tests and the Double Mass Curve method were employed to identify the variation characteristics and breakpoints of precipitation (P), potential evapotranspiration (ET0), and runoff depth (R) in the Wanquan River Basin (WQRB) during the 1970–2016 period. The changes in runoff attributed to P, ET0, and the catchment characteristics parameter (n) were quantified using the elastic coefficient method based on the Budyko hypothesis. The results revealed that the P and R in the WQRB exhibited statistically insignificant decreasing trends, while ET0 displayed a significant increasing trend (p &lt; 0.05). The breakpoint of runoff changes in the Jiabao and the Jiaji stations occurred in 1991 and 1983, respectively. The runoff changes show a negative correlation with both the n and ET0, while exhibiting a positive correlation with P. Moreover, it is observed that P and ET0 display higher sensitivity towards runoff changes compared to n. The decomposition analysis reveals that in the Dingan River Basin (DARB), human activities account for 53.54% of the runoff changes, while climate variability contributes to 46.46%. In the Main Wanquan River Basin (MWQRB), human activities contribute to 46.11%, whereas climate variability accounts for 53.89%. The research findings suggest that runoff is directly reduced by climate variability (due to decreased P and increased ET0), while human activities indirectly contribute to changes in runoff through n, exacerbating its effects. Rubber forest stands as the prevailing artificial vegetation community within the WQRB. The transformation of natural forests into rubber plantations constitutes the primary catalyst for the alteration of n in the WQRB. The research findings provide important reference for quantifying the driving force of hydrological changes caused by deforestation, which is of great significance for sustainable management of forests and water resources.

Evaluating and Predicting Meteorological Drought Using Different Climate Reanalysis Datasets over New South Wales, Australia

AbstractDroughts are one of the most disastrous natural hazards, primarily due to their persistence and spatial distribution. Drought prediction is one of the key challenges for effective drought management and to do so, studies often involve the use of station-based data which are effective only in regions with high-gauge density. Therefore, there is growing interest in the use of interpolated climatic grids to predict droughts. In recent decades, drought conditions have been aggravated by climate change and for that reason the use of climatic variables is important to accurately predict droughts. The analysis of any aspect of drought can be affected by the choice of data and drought index. Therefore, this study aims to identify the most suitable dataset and drought index for the New South Wales (NSW) region of Australia. The present study evaluates various precipitation datasets (Climate Research Unit (CRU), ERA-5, and Scientific Information for Land Owners (SILO)) and their corresponding variations on the Standardised Precipitation Index (SPI) at different time scales. Based on the findings, CRU was used to predict meteorological drought using machine learning techniques. The different machine learning models are Support Vector Regression, Random Forest and Artificial Neural Networks. The results suggest SVM to be the best performing model among these models for predicting SPI at short time scales (1 month and 3 month) and ANN to be the best performing model for long-term scales (6 months and 12 months). Such findings depict the capabilities of different models in examining drought characteristics and confirming the use of interpolated climatic grids thereby assisting in regional drought management.

Sustainability of opioid agonist therapy programmes in Belarus, the Republic of Moldova, Tajikistan and Ukraine in the context of transition from Global Fund support during 2020–2023

BackgroundMost national programmes of opioid agonist therapy (OAT) in Eastern Europe and Central Asia are at a critical juncture for building their sustainability due to decreasing support from the Global Fund and other international HIV funders. Therefore, it is timely to identify the status, trends, opportunities and risk factors of OAT preparedness in the face of donor transition.MethodsThe study assessed the OAT sustainability progress in 4 countries: Belarus, the Republic of Moldova, Tajikistan and Ukraine. The study used a comparative country case study design with qualitative methods and two data points in 2020 and 2022–2023. In total, 363 sources were reviewed and used, 83 interviews with key informants and 13 focus groups were conducted with clients, using a joint methodology and a defined Framework with three dimensions: ‘Policy & Governance’; ‘Finance & Resources’; and, ‘Services’.ResultsAll four countries have made improvements to increase OAT sustainability, though it varied. In 2022, Ukraine had a substantial degree of sustainability, followed by Belarus and Moldova with a moderate degree, while Tajikistan’s sustainability was at moderate-to-high risk. No country achieved a high degree of OAT sustainability in any of the three dimensions measured. However, a high degree of sustainability was reported for at least one indicator in Belarus, Moldova and Ukraine: ‘Medicines’; ‘Financial resources’; ‘Evidence and information systems’; ‘Service Accessibility’; or, ‘Service integration & quality’. On average, the greatest improvement between 2020 and 2022 was seen for ‘Availability & coverage’; ‘Financial resources’; ‘Service quality & integration’; and, ‘Service accessibility’. The highest risks across the countries, notably in Belarus and Tajikistan, were recorded for the indicator, ‘Availability and coverage’. Of concern is that the least progress, or even a decline, was found in ‘Human resources’.ConclusionsOAT sustainability in the 4 analysed countries remains at risk, despite progress in all countries. Managing HIV donor transition can have positive effects in addressing financial sustainability, especially inspired by Ukraine’s continued progress despite economic contraction and Russia’s invasion. More attention is needed to non-financial aspects of OAT sustainability in donor transition planning. The directions that could have multifaceted positive influence for OAT long-term resilience and scale up for impact on drug problems include decentralisation outside of health settings and broader drug treatment financial and management transformation, together with drug policy reforms. Thus far, viable solutions for sustainability of OAT in conflict areas appear unlikely. Building OAT resilience should remain high on the agenda of national stakeholders, technical partners and donors.

Public Health Nurses' Competence Related to Long-Term Breastfeeding in the Context of Maternity and Child Health Clinics.

To explore public health nurses' competence (namely knowledge, skills, and attitudes) in relation to long-term breastfeeding and their experience of the need for additional training on the subject. The study design was quantitative, descriptive, and cross-sectional. Public health nurses (n = 270). Data were collected with the Long-Term Breastfeeding Competence Scale (LBCS) online survey. Data analysis was done with Spearman's correlation analysis and binary logistic regression analysis. Slightly more than half of the respondents had a good level of knowledge and skills. The majority had a baseline positive attitude toward long-term breastfeeding, but the attitude became more negative as the age of the breastfed child increased. Better competence was associated with younger age, parenthood, an additional degree in midwifery, and breastfeeding specialist certification. Knowledge and skills, and attitudes revealed a high correlation: the higher the knowledge and skills level, the more positive attitudes. Respondents with better knowledge and skills experienced more often the need for additional training on the subject. This study addresses that public health nurses lack competence in relation to long-term breastfeeding. This may compromise the quality of breastfeeding guidance for families in healthcare settings.

Relapse-Associated and Relapse-Independent Contribution to Overall Expanded Disability Status Scale Progression in Multiple Sclerosis Patients Diagnosed in Different Eras.

The introduction of disease-modifying therapies for multiple sclerosis (MS) has led to a deceleration of disease course over the years. Although decreased relapse rate constitutes a factor, the role of relapse-associated worsening (RAW) and progression independent of relapse activity (PIRA) in MS course deceleration is still unclear. We retrospectively examined long-term Expanded Disability Status Scale (EDSS) progression in patients referred to the MS Center of Montichiari (Italy) and diagnosed with relapsing-remitting MS from 1980 to 2022. To isolate PIRA, we deducted all EDSS changes associated with relapses from overall EDSS change. We compared the relative contribution of PIRA and RAW to EDSS progression in patients diagnosed in different periods using mixed-effects models. A total of 1,405 patients were included in the study, of whom 231 were diagnosed in 1980-1996 (pre-treatment era), 577 in 1997-2008 (injectable disease-modifying therapy era), and 597 after 2008 (oral drugs, monoclonal antibodies, and anti-CD20 era). Across ages, both PIRA and RAW were reduced in patients diagnosed in more recent periods as compared with earlier periods. The average contribution of PIRA to overall EDSS progression was already predominant in patients diagnosed in 1980-1996 (78%) and in 1997-2008 (76%), but it was significantly increased (p = 0.0009) in patients diagnosed in later years (87%). The deceleration of MS course observed throughout the years is determined not only by fewer RAW events, but also by a reduction in PIRA. However, the shift toward a mostly relapse-independent progression highlights the importance of evaluating new therapies based on their effect on PIRA. ANN NEUROL 2024.

Modelling and validating soil carbon dynamics at the long-term plot scale using the rCTOOL R package

We introduce rCTOOL, an open-source R package for carbon (C) turnover modelling, featuring comprehensive documentation and a user-friendly interface. As an enhanced version of the widely used Danish C-TOOL model, rCTOOL maintains minimal input data requirements and reliable performance, while addressing the original model's limitations in openness and documentation. To validate rCTOOL, we analysed topsoil Soil Organic Carbon (SOC) dynamics using data from the long-term Askov straw disposal experiment (1981–2019), which quantifies the impact of varying annual straw C inputs on SOC storage. Our validation shows an unbiased global prediction error of less than 10%, with a mean error of 1.1 Mg C/ha (CI -0.7–3.0 Mg C/ha). The discrepancies between observed and predicted values were primarily due to spatiotemporal variability represented by the block and year in the long-term field experiment. We demonstrate how rCTOOL is a reliable asset for diverse applications in SOC management and research.

Time dependent anti-corrosion and anti-scaling efficiency of polyepoxysuccinic acid inhibitor in simulated hard water

In this study, the time-dependent performance of a phosphorus-free corrosion and scale inhibitor primarily composed of polyoxyuccinic acid (PESA) and zinc salts was investigated in a laboratory-simulated hard water environment. Long-term static scale inhibition tests and corrosion rotation coupon tests were conducted under two temperature conditions, 45 °C and 80 °C, following established literature standards. Changes in water quality, scale morphology, metal corrosion status, and electrochemical impedance were monitored using calcium ion concentration analysis, optical microscopy imaging, X-ray diffraction (XRD), and electrochemical testing. The study revealed a progressive decline in the corrosion and scale inhibition efficiency of the phosphorus-free inhibitor in simulated hard water with calcium carbonate hardness levels of 300, 400, and 500 mg/L over time. Moreover, higher water hardness accelerated corrosion onset, suggesting that calcium carbonate deposition promotes metal corrosion under the experimental conditions. Density functional theory (DFT) calculations indicated that PESA chemically adsorbs onto both metal and calcite surfaces, underscoring its dual role in corrosion and scale inhibition. These findings underscore the intrinsic corrosion and scale inhibition capabilities of PESA, complementing its synergistic film-forming properties with Zn2+.

Two-stage coordinated scheduling of hydrogen-integrated multi-energy virtual power plant in joint capacity, energy, and ancillary service markets

With the emergence of distributed resources (DRs), developing an effective, profitable, and green management method is of great importance. Virtual power plant (VPP) can aggregate various DRs, optimize their schedules, and participate in electricity markets uniformly to earn profit. The optimal scheduling problem in spot market has been widely studied, however, few studies have extended the services of VPP to the long-term scale. This paper proposes a coordinated scheduling method for a multi-energy virtual power plant (MEVPP), considering the integration of hydrogen facilities. A holistic market framework, including capacity market, energy market (EM), and ancillary service market (ASM) is developed jointly for the coordination among the annual capacity schedule, daily energy schedule, and daily reserve schedule of MEVPP. A hybrid hydrogen storage system including daily hydrogen storage and seasonal hydrogen storage is modeled to achieve both intraday and cross-seasonal peak shaving. Stochastic programming is used to tackle the uncertainties and risk management is considered by conditional value at risk. With the help of numerical analysis in case studies, it can be concluded that the proposed coordinated scheduling method can improve the economy of MEVPP. The profit from joint market decreases the total cost by about 52 %. The capacity adequacy and flexibility of MEVPP can be improved by the coordination of capacity and reserve services. The hybrid hydrogen storage system can realize the efficient integration of hydrogen and increase hydrogen production and consumption by about 9 %.

Trophic homogeneity due to seasonal variation in nitrogen in shallow subtropical lakes

Lakes play a crucial role in the nitrogen (N) cycle, and eutrophication disrupts the balance of the nitrogen cycle within lakes, including both the N removal process and the N supplement process. However, the mechanisms by which different nutrient levels affect seasonal nitrogen variations in the water columns are not clear, especially for long-term and large- scale studies. In this study, we used 206 independent spatial samples from a total of 108 subtropical shallow lakes from four surveys in the middle and lower reaches of the Yangtze River, as well as time-case study data from Lake Taihu and Lake Donghu of up to 23 and 14 years, respectively, to analyze the changes in summer TN compared to spring (delta TN). Delta TN was significantly negatively correlated with initial spring TN concentrations, with similar trends observed in both space and time. Furthermore, the slopes of spring TN vs. delta TN varied little across lakes in both time and space, suggesting a consistent relationship between initial spring TN and summer TN changes. When initial TN or TN: TP ratio was low, N fixation by algae played a significant role in compensating for summer N removal, thus mitigating summer N reductions; when TN was high or TN: TP ratio was high, ammonia stress reduced the compensatory effect of algae and denitrification played a significant role in summer N removal, thus increasing summer N reductions. Our study suggested that no matter what the initial conditions are, lakes tend to evolve towards a common nutrient status through biological regulation.

Demographic drivers of reproductive failure in a threatened bird: Insights from a decade of data

Hatching failure affects up to 77% of eggs laid by threatened bird species, yet the true prevalence and drivers of egg fertilization failure versus embryo mortality as underlying mechanisms of hatching failure are unknown. Here, using ten years of data comprising 4,371 eggs laid by a population of a threatened bird, the hihi (Notiomystis cincta), we investigate the relative importance of infertility and embryo death as drivers of hatching failure and explore population-level factors associated with them. We show that of the 1,438 eggs that failed to hatch (33% of laid eggs) between 2010 and 2020, 83% failed due to embryo mortality, with the majority failing in the early stages of embryonic development. In the most comprehensive estimates of infertility rates in a wild bird population to date, we find that fertilization failure accounts for around 17% of hatching failure overall and is more prevalent in years where the population is smaller and more male biased. Male embryos are more likely to die during early development than females, but we find no overall effect of sex on the successful development of embryos. Offspring fathered by within-pair males have significantly higher inbreeding levels than extra-pair offspring; however, we find no effect of inbreeding nor extra-pair paternity on embryo mortality. Accurately distinguishing between infertility and embryo mortality in this study provides unique insight into the underlying causes of reproductive failure over a long-term scale and reveals the complex risks of small population sizes to the reproduction of threatened species.

Changes in river morphology and influencing factors in the upper Yellow River over the past 25 years

Changes to the morphology of the upper Yellow River (UYR) had various impacts on the surrounding ecology and society, as well as the entire basin. However, low-spatial-resolution imagery (e.g., MODIS, AVHRR) cannot capture sufficient spatial details for monitoring complex water bodies, while high-spatial-resolution imagery (e.g., SPOT, Quickbird, Ikonos) lacks spatial coverage and the revisit frequency necessary for large-scale water body monitoring. To address these limitations, this study utilized the Google Earth Engine (GEE) and ArcGIS spatial analysis tools, applied pan-sharpening to downscale Landsat imagery of the study area from 1999 to 2023, performed river extraction, and calculated the spatiotemporal changes in river morphology in the UYR using river morphological parameters (i.e. area, channel width, centerline length, sinuosity index, lateral migration rate, channel stability). The Automated Water Extraction Index (AWEIsh) effectively characterized the study area, and pan-sharpening technology improved the extraction accuracy of small water bodies. Finally, the overall accuracy and Kappa coefficient were 0.993 and 0.985, respectively. Over the past 25 years, the area and average width of the entire reach of the UYR changed significantly, with the maximum value being 1.3 times the minimum value, whereas the centerline length and sinuosity index showed no apparent changes, and the lateral migration rate varied minimally, with the average annual movement ranging from 4.67 m to 10.18 m. In typical river segments without human activity, although single-channel reaches exhibited stronger stability than multi-channel reaches, natural factors (i.e. annual precipitation, annual runoff, annual sediment discharge) had a noticeable impact on the morphology of both single-channel and multi-channel reaches. Large-scale cascade hydropower development in the UYR has significantly impacted river morphology over a short period. Meanwhile, in river sections unaffected by human activities, the changes occurred gradually. This study provides support for better understanding complex river morphologies at large regional and long-term scales and a scientific basis for water resource management and sustainable development in the UYR.

Annual cyclical variations of urban green space cold island(GSCI) effect by combining remote sensing inversion and ENVI-met simulation

Combining multiple methods offers a practical approach to studying long-term variations in the urban green space cold island(GSCI) effect. This research integrates remote sensing inversion and numerical simulation to investigate the annual cycle of the GSCI in Huachong Park, Hefei City. Initially, 59 remote sensing images from various seasons between 2010 and 2020 were retrieved using Landsat series image data and atmospheric correction methods to invert Land Surface Temperatures(LST), which preliminary identified the GSCI's annual cycle variations. Subsequently, meteorological data for Hefei from 2010 to 2020 were extracted using the Solar Terms Typical Meteorological Day(STTMD) method to obtain representative annual meteorological data. These data were then input into the ENVI-met software for numerical simulations of the study area, capturing diurnal variations of the cold island effect at 24-time points and predicting annual changes in cold island intensity. The results indicate that: (1) The GSCI exhibits an annual cycle and seasonal variations characterized by “strong in summer and weak in winter, cooler in summer and warmer in winter”; (2) A progressive relationship exists between remote sensing inversion and ENVI-met numerical simulation in studying the temporal variation of the GSCI, with the integration of these methods yielding a more comprehensive spatiotemporal analysis of the GSCI over long-term scales;(3) The STTMD method effectively simplifies representative meteorological data, progressively combining remote sensing retrievals and numerical simulations to facilitate the acquisition of comprehensive spatiotemporal variations of the green space heat effect over extended periods. These findings advance understanding of the long-term dynamics of cold island effects within urban green spaces, providing valuable insights for urban planners and environmental researchers.

Developing an annual global Sub-National scale economic data from 1992 to 2021 using nighttime lights and deep learning

The Gross Domestic Product (GDP) per capita is one of the most widely used socioeconomic indicators, serving as an integral component for climate change impact analysis. However, a national scale assessment may induce considerable bias because it conceals any internal variations within a country. The lack of a long-term sub-national scale GDP data is a substantive hinderance. Leveraging the close relationship between nighttime lights and GDP, we address this gap by developing a novel methodological framework in two steps. First, under the modeling philosophy of spatial statistics, we developed a novel approach based on deep and machine learning techniques to establish a complex mapping between two inconsistent nighttime lights (NTL) datasets: the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP) and the National Polar-Orbiting Partnership’s Visible Infrared Imaging Radiometer Suite (VIIRS). The models achieve accuracies ranging from 0.945 to 0.980 (correlation coefficients). By taking the estimations ensemble of the two techniques, the time series of DMSP data was extended to 2021. Next, a novel modeling strategy based on multi-layer perceptron was developed to derive the non-linear relationship between NTL and GDP per capita at sub-national scale to alleviate scale effects at this granularity, while explicitly capturing regional heterogeneity effect. The trained models achieve average accuracies of 0.967, 0.959, and 0.959 on the training, validation, and test sets, respectively. We evaluate the developed dataset at the global, national, and sub-national scales from various perspective, and the results offer solid evidence on the reliability of the estimated economic data. By linking to historical global climate change data, we quantify global economic losses attributed to extreme heat to demonstrate how the estimated GDP data can be useful in the climate change impact analysis.

Structure and Evolution of Ribosomal Genes of Insect Chromosomes.

Currently, clusters of 45S and 5S ribosomal DNA (rDNA) have been studied in about 1000 and 100 species of the class Insecta, respectively. Although the number of insect species with known 45S rDNA clusters (also referred to as nucleolus-organizing regions, or NORs) constitutes less than 0.1 percent of the described members of this enormous group, certain conclusions can already be drawn. Since haploid karyotypes with single 45S and 5S rDNA clusters predominate in both basal and derived insect groups, this character state is apparently ancestral for the class Insecta in general. Nevertheless, the number, chromosomal location, and other characteristics of both 45S and 5S rDNA sites substantially vary across different species, and sometimes even within the same species. There are several main factors and molecular mechanisms that either maintain these parameters or alter them on the short-term and/or long-term scale. Chromosome structure (i.e., monocentric vs. holokinetic chromosomes), excessive numbers of rRNA gene copies per cluster, interactions with transposable elements, pseudogenization, and meiotic recombination are perhaps the most important among them.

Efficacy of Deep Brain Stimulation for Treatment-Resistant Depression: Systematic Review and Meta-Analysis

BackgroundTreatment-resistant depression affects about 30% of individuals with major depressive disorder. Deep brain stimulation is an investigational intervention for treatment-resistant depression with varied results. We undertook this meta-analysis to synthesize outcome data across trial designs, anatomical targets, and institutions to better establish efficacy and side-effect profiles. MethodsWe conducted a systematic PubMed review following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Seven randomized controlled trials (n = 198) and 8 open-label trials (n = 77) were included spanning 2009 to 2020. Outcome measures included Hamilton Depression Rating Scale or Montgomery–Åsberg Depression Rating Scale scores, as well as response and remission rates over time. Outcomes were tracked at the last follow-up and quantified as a time course using model-based network meta-analysis. Linear mixed models were fit to individual patient data to identify covariates. ResultsDeep brain stimulation achieved 47% improvement in long-term depression scale scores, with an estimated time to reach 50% improvement of around 23 months. There were no significant subgroup effects of stimulation target, time of last follow-up, sex, age of disease onset, or duration of disease, but open-label trials showed significantly greater treatment effects than randomized controlled trials. Long-term (12–60 month) response and remission rates were 48% and 35%, respectively. The time course of improvement with active stimulation could not be adequately distinguished from that with sham stimulation, when available. ConclusionsDeep brain stimulation produces significant chronic improvement in symptoms of treatment-resistant depression. However, the limited sham-controlled data do not demonstrate significant improvement over placebo. Future advancements in stimulation optimization and careful blinding and placebo schemes are important next steps for this therapy.

Research on Runoff Prediction Based on Time2Vec-TCN-Transformer Driven by Multi-Source Data

Due to the frequent occurrence of extreme weather in recent years, accurate runoff prediction is crucial for the rational planning and management of water resources. Addressing the high uncertainty and multiple influencing factors in runoff prediction, this paper proposes a runoff prediction method driven by multi-source data. Based on multivariate observed data of runoff, water level, temperature, and precipitation, a Time2Vec-TCN-Transformer model is proposed for runoff prediction research and compared with LSTM, TCN, and TCN-Transformer models. The results show that the Time2Vec-TCN-Transformer model outperforms other models in metrics including MAE, RRMSE, MAPE, and NSE, demonstrating higher prediction accuracy and reliability. By effectively combining Time2Vec, TCN, and Transformer, the proposed model improves the MAPE for forecasting 1–4 days in the future by approximately 7% compared to the traditional LSTM model and 4% compared to the standalone TCN model, while maintaining NSE consistently between 0.9 and 1. This model can better capture the periodicity, long-term scale information, and relationships among multiple variables of runoff data, providing reliable predictive support for flood forecasting and water resources management.

Data extension of high-resolution wind speed database by fusing meteorological observation and local objective analysis data with POD–LSE

Newly generated high-resolution meteorological reanalysis data have been published over the past few years on short records. Producing high-resolution data in long-term time scales is challenging due to high computational costs and complex weather models. In contrast, urban meteorological observations have been continuously recorded for decades, although at a low spatial resolution. This study proposes a data fusion method using Proper Orthogonal Decomposition (POD) and Linear Stochastic Estimation (LSE). It fuses meteorological observations and high spatial resolution local objective analysis (LA) data to reproduce a high-resolution and long-term fusion database for urban areas without observations. First, the accuracy of LA data was verified by comparison with meteorological observations. Subsequently, the accuracy of the generated fusion data was investigated by comparison with Doppler lidar observations. Finally, the performance of single- and multi-time LSE in data fusion is discussed. These results indicate that the proposed method generates high-resolution and long-term fusion wind speed data that are similar to the Doppler lidar observations. In addition, the fusion model built using hourly data can accurately generate a highly sampled database at 10-min intervals. The single-time LSE method is recommended to be applied over the multi-time delay method for data fusion.

Host-virus coevolution drives soil microbial function succession along a millennium land reclamation chronosequence

IntroductionGene exchange between viruses and hosts plays an important role in driving virus-host coevolution, enabling adaptation of both viruses and hosts to environmental changes. However, the mechanisms and functional significance of virus-host gene exchanges over long-term scales remain largely unexplored. ObjectiveThe present study aimed to gain insights into the role of viruses in virus-host interactions and coevolution by monitoring virome dynamics along a millennium-long land reclamation chronosequence. MethodsWe collected 24 soil samples from 8 stages of a millennium-long land reclamation chronosequence, including non-reclamation, and reclamation periods of 10, 50, 100, 300, 500, 700, and 1000 years. We characterized their metagenomes, and identified DNA viruses within these metagenomes. ResultsOur findings reveal a significant shift in viral community composition after 50 years of land reclamation, but soil viral diversity reached a stable phase approximately 300 years after the initial reclamation. Analysis of the virus-host network showed a scale-free degree distribution and a reduction in complexity over time, with generalist viruses emerging as key facilitators of horizontal gene transfer. ConclusionThese findings highlight the integral role of viruses, especially generalist types, in mediating gene exchanges between viruses and hosts, thereby influencing the coevolutionary dynamics in soil ecosystems over significant timescales. This study offers novel insights into long-term virus-host interactions, showing how the virome responds to environmental changes, driving shifts in various microbial functions in reclaimed land.

The adaptive mechanisms of the marine diatom Thalassiosira weissflogii to long-term high CO2 and warming.

While it is known that increased dissolved CO2 concentrations and rising sea surface temperature (ocean warming) can act interactively on marine phytoplankton, the ultimate molecular mechanisms underlying this interaction on a long-term evolutionary scale are relatively unexplored. Here, we performed transcriptomics and quantitative metabolomics analyses, along with a physiological trait analysis, on the marine diatom Thalassiosira weissflogii adapted for approximately 3.5 years to warming and/or high CO2 conditions. We show that long-term warming has more pronounced impacts than elevated CO2 on gene expression, resulting in a greater number of differentially expressed genes (DEGs). The largest number of DEGs was observed in populations adapted to warming + high CO2, indicating a potential synergistic interaction between these factors. We further identified the metabolic pathways in which the DEGs function and the metabolites with significantly changed abundances. We found that ribosome biosynthesis-related pathways were upregulated to meet the increased material and energy demands after warming or warming in combination with high CO2. This resulted in the upregulation of energy metabolism pathways such as glycolysis, photorespiration, the tricarboxylic acid cycle, and the oxidative pentose phosphate pathway, as well as the associated metabolites. These metabolic changes help compensate for reduced photochemical efficiency and photosynthesis. Our study emphasizes that the upregulation of ribosome biosynthesis plays an essential role in facilitating the adaptation of phytoplankton to global ocean changes and elucidates the interactive effects of warming and high CO2 on the adaptation of marine phytoplankton in the context of global change.