Historical Estimates Research Articles

Assessing temporal dynamics of nitrogen surplus in Indian agriculture: district scale data from 1966 to 2017

Nitrogen (N) is essential for agricultural productivity, yet its surplus poses significant environmental risks. Currently, over half of applied nitrogen is lost, resulting in resource wastage, contributing to increased greenhouse gas emissions and biodiversity loss. Excess nitrogen persists in the environment, contaminating soil and water bodies for decades. Quantifying detailed historical N-surplus estimation in India remains limited, despite national and global-scaled assessments. Our study develops a district-level dataset of annual agricultural N-surplus from 1966-2017, integrating 12 different estimates to address uncertainties arising from multiple data sources and methodological choices across major elements of the N surplus. This dataset supports flexible spatial aggregation, aiding policymakers in implementing effective nitrogen management strategies in India. In addition, we verified our estimates by comparing them with previous studies. This work underscores the importance of setting realistic nitrogen management targets that account for inherent uncertainties, paving the way for sustainable agricultural practices in India, reducing environmental impacts, and boosting productivity.

CNS Relapse in High-Grade B-cell Lymphoma with MYC and BCL2 Rearrangements and Dark-Zone Signature Expressing DLBCL

High-grade B-cell lymphoma with MYC and BCL2 rearrangements (HGBCL-DH-BCL2), or ‘double-hit lymphoma,’ has been associated with a high risk of central nervous system (CNS) relapse. However, historic estimates are impacted by selection bias. We report CNS relapse rates associated with HGBCL-DH-BCL2 from a population-based cohort with complete fluorescence in situ hybridization testing, as well as diffuse large B-cell lymphoma morphology (DLBCL) tumors expressing the dark-zone gene expression signature (DZsig), which was originally derived from HGBCL-DH-BCL2. The 2-year CNS relapse risk in HGBCL-DH-BCL2 was 6.8%. CNS relapses were early, predominantly leptomeningeal (73%) and co-occurred with systemic relapse (64%). High-risk CNS International Prognostic Index (CNS-IPI) and concordant bone marrow involvement were associated with an elevated CNS relapse risk in HGBCL-DH-BCL2. The ‘refined cell of origin’ classification assigned 20% of DLBCL morphology tumors with germinal center B-cell-like phenotype (GCB-DLBCL) into a distinct subgroup based on DZsig expression (DZsig+). CNS relapse risk in DZsig+ (2-year: 6.4%) was independent of HGBCL-DH-BCL2 status and was further stratified by the CNS-IPI. CNS relapse in DZsig-negative GCB-DLBCL was rare (2-year risk 1.4%; P=.04 versus DZsig+) and exclusively parenchymal. Altogether, the CNS relapse risk in HGBCL-DH-BCL2 is lower than previously reported and DZsig refines risk stratification in GCB-DLBCL.

Bias‐Eliminating Techniques in the Computation of Power Spectra for Characterizing Gravity Waves: Interleaved Methods and Error Analyses

AbstractObservational data inherently contain noise which manifests as uncertainties in the measured parameters and creates positive biases or noise floors in second‐order products like variances, fluxes, and spectra. Historical methods estimate and subsequently subtract noise floors, but struggle with accuracy. Gardner and Chu (2020, doi.org/10.1364/AO.400375) proposed an interleaved data processing method, which inherently eliminates biases from variances and fluxes, and suggested that the method could also eliminate noise floors of power spectra. We investigate the interleaved method for spectral analysis of atmospheric waves through theoretical studies, forward modeling, and demonstration with lidar data. Our work shows that calculating the cross‐power spectral density (CPSD) from two interleaved subsamples does reduce the spectral noise floor significantly. However, only the Co‐PSD (the real part of CPSD) eliminates the noise floor completely, while taking the absolute magnitude of CPSD adds a reduced noise floor back to the spectrum when the sample number is finite. This reduced noise floor can be further minimized through averaging over more observations, completely different from traditional spectrum calculations whose noise floor cannot be reduced by incorporating more samples. We demonstrate the first application of the interleaved method to spectral data, successfully eliminating the noise floor using the Co‐PSD in a forward model and in lidar observations of the vertical wavenumber of gravity waves at McMurdo, Antarctica. This high accuracy is gained by sacrificing precision due to photon‐count splitting, requiring additional observations to counter this effect. We provide quantitative assessment of accuracy and precision as well as application recommendations.

Comparison of a radiofrequency electric and magnetic field source-based job-exposure matrix with personal radiofrequency exposure measurements.

Assessing occupational exposure to radiofrequency electromagnetic fields (RF-EMF) presents significant challenges due to the considerable variability in exposure levels within and between occupations. This spatial and temporal variability complicates the reliable evaluation of potential health risks associated with RF-EMF exposure in the workplace. Accurate assessment methods are crucial to understand the extent of exposure and to evaluate potential health risks, especially given the potential for higher exposures in occupational settings compared to the general population. This study compares the historical RF-EMF exposure estimates in the INTEROCC RF-EMF job-exposure matrix (RF-JEM) with recent personal measurement data collected in 2 countries as part of the OccRF-Health study, to assess the broader applicability of the RF-JEM. Weighted kappa (k w) coefficients and Spearman rank correlation tests were performed to assess the alignment between RF-JEM estimates and measurements for 8h time-weighted average exposure intensity and prevalence estimates across various occupations. The comparisons were mainly based on 22 jobs having ≥5 measured workers in the OccRF-Health study. Poor agreement was found for both exposure prevalence and intensity between both methods (k w < 0.1). RF-JEM values likely overestimated exposure levels for both electric (E) and magnetic (H) fields (mean percentage difference >194%) compared to current personal measurements. Findings suggest that the INTEROCC-JEM likely overestimates current exposure intensity levels in the measured jobs. Adopting a semiquantitative JEM could also mitigate misclassification errors due to exposure variability, improving accuracy in exposure assessment. These findings indicate the need for more targeted personal measurements, including among highly exposed workers, and for potentially considering new exposure metrics to more accurately assess occupational RF-EMF exposures in occupational epidemiological research.

Comment on ‘An examination of the ability of plantain (Plantago lanceolata L.) to mitigate nitrogen leaching from pasture systems’

ABSTRACT In their review: An examination of the ability of plantain (Plantago lanceolata L.) to mitigate nitrogen leaching from pasture systems, Eady et al. (2024) dispute both the historic estimates of typical urine patch nitrogen (N) load and leaching and the evidence for the N leaching reduction mechanisms of plantain; and question the recommended levels of plantain required to achieve N leaching reduction. We reject the suggestion that the urine patch has little influence on N leaching, and that average annual N leaching from dairy farms is 6 kg N/ha. We agree that the low dry matter content of plantain is the dominant and best documented effect of plantain on urine N dilution. We reject that there is no evidence for the effect of plantain on nitrogen partitioning to urine, and on potential nitrification rate in the urine patch. We point to empirical evidence of statistically significant reductions in nitrate leaching from plantain at paddock scale, at levels as low as 21% plantain of dry matter eaten. Current research will improve understanding of the mechanisms and magnitude of the effect of plantain on N loss, paving the way for recognition of other forage-based N loss reduction options, and ongoing development of mechanistic models that are adaptable to other forages.

Disaggregation and Nowcasting of Regional GDP Series with a Simple Smoothing Algorithm

We propose a simple smoothing method for the spatiotemporal disaggregation of economic time series. Contrary to the existing methods, our approach does not require exogenous regressors and can therefore be used for countries lacking long and reliable series of regional economic indicators. The proposed method can also be applied sequentially, implying that one needs to revise only the estimates for the last low-frequency period when new data are disaggregated. This is a convenient feature when historical estimates are of interest. We apply this method to disaggregate annual real GDP data for Polish regions into quarterly series and compare the results with the series obtained from a multivariate linear regression-based procedure, considering the differences between the estimates and the consequences for regional recession dating. We also examine the nowcasting performance of the smoothing algorithm and find that it is superior to the regression-based alternative for most of the studied sample lengths and horizons up to a year.

First Red List of Ecosystems assessment of a tropical glacier ecosystem to diagnose the pathways towards imminent collapse

Abstract Tropical glaciers are rapidly disappearing, particularly in isolated mountain peaks below 5,000 m elevation. These glaciers are fundamental substrates for unique cryogenic ecosystems in high tropical environments where the ice, melting water and rocky substrate sustain microbiological communities and other meso- and macro-biota. This study uses the Red List of Ecosystems guidelines to diagnose the collapse of the tropical glacier ecosystem of the Cordillera de Mérida, Venezuela. We undertook the assessment with existing estimates of glacier ice extent, indirect historical estimates of ice mass balance and global mechanistic models of future ice mass balance. We complemented these with additional statistical analysis of trends and bioclimatic suitability modelling to calculate and predict rates of decline and relative severity of degradation in selected ecosystem indicators. The evidence suggests an extreme risk of collapse (Critically Endangered) because of a prolonged and acute reduction in ice extent and changes in climatic conditions that are leading to the complete loss of ice mass. The ice substrate has declined 90% in the last 20 years, and observed acceleration of the rate of decline suggests it will probably disappear within the next 5 years. Loss of ice substrate will trigger an immediate loss of supraglacial, englacial and subglacial biotic compartments and initiate a decades-long succession of forefield vegetation. However, ongoing inventories of native biota and monitoring of ecosystem transitions can provide valuable insights and lessons for other ecosystems facing similar risks. The Red List of Ecosystems assessment protocol provides a useful framework for comparative analysis of cryogenic ecosystems.

Does rafting promote contemporary gene flow? Global and regional patterns of population genetic diversity and structure on the false limpet Siphonaria lateralis in the Southern Ocean

Rafting has been proposed as an effective mechanism for species without free-living pelagic larvae to achieve long-distance dispersal, theoretically preventing population differentiation over wide distributional ranges. Moreover, rafting has been advocated as a main dispersal mechanism for marine invertebrates with sub-Antarctic distributions, because of abundant buoyant kelps, driven by the Antarctic Circumpolar Current. Nonetheless, little attention has been given to the role of rafting to establish regular gene flow across the sub-Antarctic, and the geographic and temporal scales at which it occurs. Aiming to unravel these major questions about the extent of genetic connectivity across the Southern Ocean (SO), we studied the pulmonate limpet Siphonaria lateralis, a benthic species with encapsulated larvae, found on the rocky intertidal of sub-Antarctic islands and southern South America. Since S. lateralis is closely associated with D. antarctica, dispersal by rafting is plausible, as revealed by the absence of phylogeographic structure across the sub-Antarctic. We sampled 116 individuals from eight localities across the SO, and used 5,515 SNPs obtained through Genotyping-by-Sequencing, to determine contemporary genetic diversity, structure, and gene flow at two spatial scales; global, across the SO, and regional, within Kerguelen. Results identified substantial genetic structure, differentiating Patagonia, Falklands/Malvinas Islands, South Georgia and the Kerguelen archipelago, and low levels of contemporary gene flow. The most notable genetic differentiation was found between Patagonia/Falklands and South Georgia/Kerguelen. Structure was also significant between Patagonia and the Falkland/Malvinas Islands. Conversely, South Georgia and Kerguelen exhibited closer genetic affinity, and indications of recent but limited gene flow. Moreover, historical gene flow estimates between the four populations were low. At regional scale, noteworthy genetic structure persisted, and gene flow was insufficient to prevent genetic differentiation within Kerguelen. Consequently, rafting’s potential may be overestimated as a contemporary mechanism promoting gene flow across the SO, as these events may be sporadic, irregular, and unpredictable for marine invertebrates lacking a larval dispersal stage, since contemporary dispersal events don’t seem to facilitate high gene flow at both scales. Accordingly, other oceanographic factors or processes may hinder the establishment of species associated with macroalgae, and as consequence, contemporary genetic connectivity in the sub-Antarctic.

Software System Architecture for Estimating Software Development Time

The article is devoted to the issues of estimating the duration of software development, the solution of which can significantly improve the efficiency of software projects. The relevance of the topic is conditioned by the rapid development of software development methodologies, on which the existing algorithms of such estimation depend, and, as a consequence, by the rapid obsolescence of approaches to solving this problem. A systematic analysis of the main algorithms for estimating software development duration is carried out. The classification of such algorithms is presented. The algorithm of software development duration estimation and the idea of the system implementing it, which will allow end users to automate this process, are proposed. The approaches to implementation of algorithms of retrospective (historical) estimation, PERT, expert estimation are considered. The prospectivity of researches in the field of implementation of methods of expert evaluation using neural networks is shown. The architecture of the proposed system is described. A prototype of the system implementing the method of retrospective evaluation based on the evaluation of the speed of the development team is developed.

Will there be water? Climate change, housing needs, and future water demand in California

Climate change in California is expected to alter future water availability, impacting water supplies needed to support future housing growth and agriculture demand. In groundwater-dependent regions like California's Central Coast, new land-use related water demand and decreasing recharge is already stressing depleted groundwater basins. We developed a spatially explicit state-and-transition simulation model that integrates climate, land-use change, water demand, and groundwater gain-loss to examine the impact of future climate and land use change on groundwater balance and water demand in five counties along the Central Coast from 2010 to 2060. The model incorporated downscaled groundwater recharge projections based on a Warm/Wet and a Hot/Dry climate future from a spatially explicit hydrological process-based model. Two urbanization projections from a parcel-based, regional urban growth model representing 1) recent historical and 2) state-mandated housing growth projections were used as alternative spatial targets for future urban growth. Agricultural projections were based on recent historical trends from remote sensing data. Annual projected changes in groundwater balance were calculated as the difference between land-use related water demand, based on historical estimates, and climate-driven recharge plus agriculture return flows. Results indicate that future changes in climate-driven groundwater recharge, coupled with cumulative increases in agricultural water demand, result in overall declines in future groundwater balance, with a Hot/Dry future resulting in cumulative groundwater decline in all but Santa Cruz County. Cumulative declines by 2060 are especially prominent in San Luis Obispo (−2.9 to −5.1 Bm3) and Monterey counties (−6.5 to −8.7 Bm3), despite limited changes in agricultural water demand over the model period. These two counties show declining groundwater reserves in a Warm/Wet future as well, while San Benito and Santa Barbara County barely reach equilibrium. These results suggest future groundwater supplies may not be able to keep pace with regional demand and declining climate-driven recharge, resulting in a potential reduction in water security in the region. However, our county-scale projections showed new housing and associated water demand does not conflict with California's groundwater sustainability goals. Rather, future climate coupled with increasing agricultural groundwater demand may reduce water security in some counties, potentially limiting available groundwater supplies for new housing.

Long-term changes of methane emissions from rice cultivation during 2000 – 2060 in China: Trends, driving factors, predictions and policy implications

Regional budget assessments of methane (CH4) are critical for future climate and environmental management. CH4 emissions from rice cultivation (CH4-rice) constitute one of the most significant sources. However, previous studies mainly focus on historical emission estimates and lack consideration of future changes in CH4-rice under climate change or anthropogenic policy intervention, which hampers our understanding of long-term trends and the implementation of targeted emission reduction efforts. This study investigates the spatiotemporal variations of CH4-rice over the past two decades, using an integrated method to identify the major drivers and predict future emissions under climate change scenarios and policy perspectives. Results indicate that the CH4-rice emissions in China ranged between 6.21 and 6.57 Tg yr−1 over the past two decades, with a spatial distribution characterized by decreases in the south and increases in the north, associated with economic development, dietary shifts, technological advancements, and climate change. Factors such as the rate of straw added (RSA), fertilization, soil texture, temperature, and precipitation significantly influence CH4 emissions per unit rice production (CH4-urp), with RSA identified as the most significant tillage management factor, explaining 32 % of the variance. Lowering RSA to 8 % is beneficial for reducing CH4-urp. Scenario analysis indicates that under policies focusing on production or demand, CH4-rice is expected to increase by 0.3 % to 5.6 %, while adjusting RSA can reduce CH4-rice by 9.4 % to 10.0 %. Structural adjustments and regional cooperation serve as beneficial starting points for controlling and reducing CH4-rice in China, while optimizing industrial layouts contributes to regional development and CH4-rice control. Implementing policies related to maintaining field and crop yields can achieve a balance between rice supply and demand ahead of schedule. Dynamic adjustment of rice cultivation based on supply–demand balance can effectively reduce CH4-rice from excess rice production. By 2060, the reduction effect could reach 8.95 %–12.01 %. Introducing policy-driven tillage management measures as reference indicators facilitates the reduction of CH4-rice.

Changed Seasonality and Forcings of Peak Annual Flows in Ephemeral Channels at Flagstaff, Northern Arizona, USA

Flood variability associated with urbanization, ecological change, and climatic change is of increasing economic and social concern in and around Flagstaff, Arizona, where flood hydrology is influenced by a biannual precipitation regime and the relatively unique geologic setting at the edge of the San Francisco Volcanic Field on the southern edge of the Colorado Plateau. There has been limited long-term gauging of the ephemeral channels draining the developed lands and dry coniferous forests of the region, resulting in a spaciotemporal gap in observation-based assessments of large-scale flooding patterns. We present new data from over 10 years of flood monitoring using a crest stage gauge network, combined with other channel monitoring records from multiple agency sources, to assess inter-decadal patterns of flood change in the area, with a specific emphasis on examining how various controls and disturbances have altered the character and seasonality of peak annual flows. Methods of analysis included the following: using Fisher’s Exact Test to compare the seasonality of flooding between historic data spanning the 1970s and contemporary data obtained since 2010; summarizing GIS-based spatial data and meteorological timeseries to characterize study catchment conditions and changes between flood study periods; and relating spatiotemporal patterns of flood seasonality and occurrences of notably large floods with catchment characteristics and environmental changes. Our results show systematic patterns and changes in Flagstaff-area flood regimes that relate to geologic and topographic controls of the varied catchment systems, and in response to records of climate variations and local catchment disturbances, including urbanization and, especially, high-severity wildfire. For most catchments there has been a shift from predominantly late winter to spring snowmelt floods, or mixed seasonal flood regimes, towards monsoon-dominated flooding, patterns which may relate to observed local warming and precipitation changes. Post-wildfire flooding has produced extreme flood discharges which have likely exceeded historical estimates of flood magnitude over decade-long monitoring periods by one to two orders of magnitude. We advocate for continued monitoring and the expansion of local stream gauge networks to enable seasonal, magnitude-frequency trend analyses, improved climate and environmental change attribution, and to better inform the many planned and ongoing flood mitigation projects being undertaken in the increasingly developed Flagstaff region.

Epidemiological inference at the threshold of data availability: an influenza A(H1N2)v spillover event in the United Kingdom.

Viruses that infect animals regularly spill over into the human population, but individual events may lead to anything from a single case to a novel pandemic. Rapidly gaining an understanding of a spillover event is critical to calibrating a public health response. We here propose a novel method, using likelihood-free rejection sampling, to evaluate the properties of an outbreak of swine-origin influenza A(H1N2)v in the United Kingdom, detected in November 2023. From the limited data available, we generate historical estimates of the probability that the outbreak had died out in the days following the detection of the first case. Our method suggests that the outbreak could have been said to be over with 95% certainty between 19 and 29 days after the first case was detected, depending upon the probability of a case being detected. We further estimate the number of undetected cases conditional upon the outbreak still being live, the epidemiological parameter R 0, and the date on which the spillover event itself occurred. Our method requires minimal data to be effective. While our calculations were performed after the event, the real-time application of our method has potential value for public health responses to cases of emerging viral infection.

Per Capita Cropland Estimations for Traditional Agricultural Areas of China over Past Millennium

Studying changes in land use per capita is critical for understanding the interactions between humans and ecosystems, and for modeling the impacts of land use changes on climate systems. However, many uncertainties in historical estimates significantly hinder climate modeling. This study estimated the per capita cropland area in traditional agricultural regions of China over the past millennium using historical-document-based and modern statistical cropland and population data. The findings showed that changes in the per capita cropland area in the provinces of the middle and lower reaches of the Yellow River could be characterized into three stages: slow decrease, rapid increase, and fluctuating decrease, whereas, in the provinces of the middle and lower reaches of the Yangtze River, there was a continuous decrease. Spatially, the per capita cropland area was higher in the middle and lower reaches of the Yellow River and lower in the middle and lower reaches of the Yangtze River during the study period. The per capita cropland areas showed clear differences in the HYDE dataset and our study; the corresponding values of our study were 2.1–8.0, 1.7–8.2, and 1.6–8.8 times higher than those from the HYDE dataset for the early Song, Yuan, and Ming dynasties, respectively.

Using the classical model for structured expert judgment to estimate extremes: a case study of discharges in the Meuse River

Abstract. Accurate estimation of extreme discharges in rivers, such as the Meuse, is crucial for effective flood risk assessment. However, hydrological models that estimate such discharges often lack transparency regarding the uncertainty in their predictions. This was evidenced by the devastating flood that occurred in July 2021, which was not captured by the existing model for estimating design discharges. This article proposes an approach to obtain uncertainty estimates for extremes with structured expert judgment using the classical model (CM). A simple statistical model was developed for the river basin, consisting of correlated generalized extreme value (GEV) distributions for discharges from upstream tributaries. The model was fitted to seven experts' estimates and historical measurements using Bayesian inference. Results were fitted only to the measurements were solely informative for more frequent events, while fitting only to the expert estimates reduced uncertainty solely for extremes. Combining both historical observations and estimates of extremes provided the most plausible results. The classical model reduced the uncertainty by appointing the most weight to the two most accurate experts, based on their estimates of less extreme discharges. The study demonstrates that with the presented Bayesian approach that combines historical data and expert-informed priors, a group of hydrological experts can provide plausible estimates for discharges and potentially also other (hydrological) extremes with relatively manageable effort.

Slow recovery rates and spatial aggregation of Triatoma infestans populations in an area with high pyrethroid resistance in the Argentine Chaco

BackgroundThe emergence of pyrethroid resistance has threatened the elimination of Triatoma infestans from the Gran Chaco ecoregion. We investigated the status and spatial distribution of house infestation with T. infestans and its main determinants in Castelli, a municipality of the Argentine Chaco with record levels of triatomine pyrethroid resistance, persistent infestation over 2005–2014, and limited or no control actions over 2015–2020.MethodsWe conducted a 2-year longitudinal survey to assess triatomine infestation by timed manual searches in a well-defined rural section of Castelli including 14 villages and 234 inhabited houses in 2018 (baseline) and 2020, collected housing and sociodemographic data by on-site inspection and a tailored questionnaire, and synthetized these data into three indices generated by multiple correspondence analysis.ResultsThe overall prevalence of house infestation in 2018 (33.8%) and 2020 (31.6%) virtually matched the historical estimates for the period 2005–2014 (33.7%) under recurrent pyrethroid sprays. While mean peridomestic infestation remained the same (26.4–26.7%) between 2018 and 2020, domestic infestation slightly decreased from 12.2 to 8.3%. Key triatomine habitats were storerooms, domiciles, kitchens, and structures occupied by chickens. Local spatial analysis showed significant aggregation of infestation and bug abundance in five villages, four of which had very high pyrethroid resistance approximately over 2010–2013, suggesting persistent infestations over space-time. House bug abundance within the hotspots consistently exceeded the estimates recorded in other villages. Multiple regression analysis revealed that the presence and relative abundance of T. infestans in domiciles were strongly and negatively associated with indices for household preventive practices (pesticide use) and housing quality. Questionnaire-derived information showed extensive use of pyrethroids associated with livestock raising and concomitant spillover treatment of dogs and (peri) domestic premises.ConclusionsTriatoma infestans populations in an area with high pyrethroid resistance showed slow recovery and propagation rates despite limited or marginal control actions over a 5-year period. Consistent with these patterns, independent experiments confirmed the lower fitness of pyrethroid-resistant triatomines in Castelli compared with susceptible conspecifics. Targeting hotspots and pyrethroid-resistant foci with appropriate house modification measures and judicious application of alternative insecticides with adequate toxicity profiles are needed to suppress resistant triatomine populations and prevent their eventual regional spread.Graphical

Spatio-temporal deep recurrent convolutional neural network for infrared focal plane arrays non-uniformity correction

The inherent non-uniformity generated in infrared focal plane arrays (IRFPA) due to inconsistent responses of detectors, and it significantly degrades the performance of infrared imaging systems. Traditional Non-Uniformity Correction (NUC) methods mainly leverage the temporal low-frequency property of non-uniformity to remove it. In recent years, Convolutional Neural Network (CNN) has gained attention as a powerful tool for conducting NUC using solely spatial information from a single frame. In this study, we propose a deep neural network with a recurrent spatio-temporal feature fusion module to enhance CNN performance using inter-frame information. Initially, the proposed network uses CNN to coarsely extract FPN features solely from the spatial domain. Subsequently, the network merges it with the historical estimation under the guidance of two gating signals, which calculated from spatio-temporal information with gated convolutional layers. Both stages are jointly trained to ensure that the entire network operates end-to-end and adapts to different scenarios, eliminating complex parameter tuning efforts. Experiments demonstrated that the proposed network shows both the advantages of traditional and CNN-based NUC methods. It has a higher convergence starting point, faster convergence speed, and a 1 ∼ 2 dB improvement in PSNR compared to both types of methods.

North and South in Medieval Iberia: A historical and environmental estimate through isotopic analyses.

The Middle Ages in the Iberian Peninsula is a period of special interest for studying the relationship of climate change with historical and socioeconomic processes. Between the 8th and 15th centuries AD, the Peninsula was characterized not only by complex political, cultural, and social transitions but also by major variations in the climate. The objective of this study was to examine differences in diet and mobility between distinct populations of the Peninsula and explore the possible relationship of diet, mobility, and culture with environmental variables and geographical settings. For this purpose, we obtained stable isotopic ratios of carbon and oxygen (δ13C and δ18O) from the enamel apatite of first upper incisors from 145 individuals at eight archeological sites that represent both Christian and Islamic communities and both rural and urban social settings. Results revealed a dietary difference between Christian and Islamic populations, observing a greater contribution of C4 plants, possibly sorghum, in the diet of the latter, especially in a rural setting. The disparity in oxygen isotopic ratios between populations from the North and South of the Peninsula is consistent with modern climatic differences between these regions. In this line, intraregional variability in oxygen isotopic ratios may hint at diachronic occupation phases under varying climatic conditions. The few isotopic outliers in our sample suggest overall low mobility levels.

Minimally destructive hDNA extraction method for retrospective genetics of pinned historical Lepidoptera specimens

The millions of specimens stored in entomological collections provide a unique opportunity to study historical insect diversity. Current technologies allow to sequence entire genomes of historical specimens and estimate past genetic diversity of present-day endangered species, advancing our understanding of anthropogenic impact on genetic diversity and enabling the implementation of conservation strategies. A limiting challenge is the extraction of historical DNA (hDNA) of adequate quality for sequencing platforms. We tested four hDNA extraction protocols on five body parts of pinned false heath fritillary butterflies, Melitaea diamina, aiming to minimise specimen damage, preserve their scientific value to the collections, and maximise DNA quality and yield for whole-genome re-sequencing. We developed a very effective approach that successfully recovers hDNA appropriate for short-read sequencing from a single leg of pinned specimens using silica-based DNA extraction columns and an extraction buffer that includes SDS, Tris, Proteinase K, EDTA, NaCl, PTB, and DTT. We observed substantial variation in the ratio of nuclear to mitochondrial DNA in extractions from different tissues, indicating that optimal tissue choice depends on project aims and anticipated downstream analyses. We found that sufficient DNA for whole genome re-sequencing can reliably be extracted from a single leg, opening the possibility to monitor changes in genetic diversity maintaining the scientific value of specimens while supporting current and future conservation strategies.

Microbial respiration in contrasting ocean provinces via high-frequency optode assays

Microbial respiration is a critical component of the marine carbon cycle, determining the proportion of fixed carbon that is subject to remineralization as opposed to being available for export to the ocean depths. Despite its importance, methodological constraints have led to an inadequate understanding of this process, especially in low-activity oligotrophic and mesopelagic regions. Here, we quantify respiration rates as low as 0.2 µmol O2 L-1 d-1 in contrasting ocean productivity provinces using oxygen optode sensors to identify size-fractionated respiration trends. In the low productivity region of the North Pacific Ocean at Station Papa, surface whole water microbial respiration was relatively stable at 1.2 µmol O2 L-1 d-1. Below the surface, there was a decoupling between respiration and bacterial production that coincided with increased phytodetritus and small phytoplankton. Size-fractionated analysis revealed that cells &amp;lt;5 µm were responsible for the majority of the respiration in the Pacific, both at the surface and below the mixed layer. At the North Atlantic Porcupine Abyssal Plain, surface whole water microbial respiration was higher (1.7 µmol O2 L-1 d-1) than in the Pacific and decreased by 3-fold below the euphotic zone. The Atlantic size-fraction contributions to total respiration shifted on the order of days during the evolution of a phytoplankton bloom with regular storm disturbances. The high-resolution optode method used in the Atlantic captured these significant shifts and is consistent with coinciding stain-based respiration methods and historical site estimates. This study highlights the dynamic nature of respiration across vertical, temporal, and size-fractionated factors, emphasizing the need for sensitive, high-throughput techniques to better understand ocean ecosystem metabolism.