bluuebunny/crossref_metadata_2025_split huggingface dataset:
Multiple-resistance evolution to ACCase inhibitors and glyphosate in sourgrass (Digitaria insularis) is attributed to diverse polymorphisms in the herbicide target sites
Authors: Moisinho Ivana Santos , Ferreira Luiz Augusto Inojosa , Krzyzaniak Fabrício , Albrecht Alfredo Junior Paiola , Albrecht Leandro Paiola , Brunharo Caio 
Published: October 2024

AbstractSourgrass [Digitaria insularis (L.) Mez ex Ekman] is considered the most troublesome weed in agronomic crops in South America. Overreliance on glyphosate has selected for resistant populations, although the resistance mechanisms remain unknown. Recently, populations were identified that exhibited multiple resistance to 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) and acetyl-CoA carboxylase (ACCase) inhibitors, posing a significant challenge due to the lack of alternative control options. This project aimed to identify the resistance patterns and levels to glyphosate and ACCase inhibitors of three suspected resistant populations (P1, P2, and P3), and elucidate the resistance mechanisms. We performed dose–response experiments with clethodim, fluazifop-P-butyl, glyphosate, and pinoxaden to identify the possibility of cross- and multiple resistance and to quantify the resistance levels. We sequenced the ACCase and EPSPS genes to test the hypothesis that target-site mutations were involved in the resistance mechanisms, given the resistance patterns observed. Our results indicated that two of the tested populations, P1 and P2, were multiple resistant to glyphosate and all ACCase-inhibitor classes, while P3 was resistant to glyphosate only. Resistance levels varied by herbicide, with resistance indices ranging from 2.7- to nearly 2,000-fold. We identified an amino acid substitution in ACCase at position 2078 (Asp-2078-Gly), homozygous for both P1 and P2, corroborating the resistance patterns observed. Interestingly, EPSPS sequencing identified multiple heterozygous DNA polymorphisms that resulted in amino acid substitutions at positions 106 (P1 and P2) or at both 102 and 106 (P3), indicating multiple evolutionary origins of glyphosate-resistance evolution. We show for the first time the genetic mechanisms of multiple resistance to glyphosate and ACCase in D. insularis, and provide a thorough discussion of the evolutionary and management implications of our work.
Interpretable Transformer Neural Network Prediction of Diverse Environmental Time Series Using Weather Forecasts
Authors: Orozco López Enrique , Kaplan David , Linhoss Anna 
Published: October 2024

AbstractTransformer neural networks (TNNs) have caused a paradigm shift in deep learning domains like natural language processing, gathering immense interest due to their versatility in other fields such as time series forecasting (TSF). Most current TSF applications of TNNs use only historic observations to predict future events, ignoring information available in weather forecasts to inform better predictions, and with little attention given to the interpretability of the model's use of explanatory inputs. This work explores the potential for TNNs to perform TSF across multiple environmental variables (streamflow, stage, water temperature, and salinity) in two ecologically important regions: the Peace River watershed (Florida) and the northern Gulf of Mexico (Louisiana). The TNN was tested and its prediction uncertainty quantified for each response variable from one‐to fourteen‐day‐ahead forecasts using past observations and spatially distributed weather forecasts. A sensitivity analysis (SA) was performed on the trained TNNs' attention weights to identify the relative influence of each input variable on each response variable across prediction windows. Overall model performance ranged from good to very good (0.78 < NSE < 0.99 for all variables and forecast horizons). Through the SA, we found that the TNN was able to learn the physical patterns behind the data, adapt the use of input variables to each forecast, and increasingly use weather forecast information as prediction windows increased. The TNN's excellent performance and flexibility, along with the intuitive interpretability highlighting the logic behind the models' forecasting decision‐making process, provide evidence for the applicability of this architecture to other TSF variables and locations.
A New Efficient Approach to Model Stiff Biogeochemical Reactive Transport Scenarios Across Groundwater Systems
Authors: Bu Xiaochuang , Dai Heng , Yuan Songhu , Ye Ming , Dai Zhenxue , Reza Soltanian Mohamad , Wen Zhang , Guadagnini Alberto 
Published: October 2024

AbstractBiogeochemical reactive transport models (RTMs) are key for understanding the evolution of the quality of groundwater systems and their interaction with anthropogenic activities. The inherent stiffness of these models, within which bio‐geochemical reactions and transport processes take place simultaneously across diverse time scales, poses significant computational challenges. The development of current RTMs is characterized by a tradeoff between accuracy and computational efficiency. Our study introduces a novel approach grounded on Computational Singular Perturbation (CSP) with the aim of efficiently solving stiff biogeochemical RTMs in groundwater systems. We integrate the CSP concept and algorithm with a reactive transport model associated with a groundwater system. Our results document that this yields a significant improvement in terms of efficiency while maintaining accuracy. For demonstration and evaluation purposes, we apply the approach to a collection of typical groundwater biogeochemical RTMs including H2O2 production/consumption, Cr(VI) adsorption‐desorption equilibrium, and denitrification processes within riparian aquifers. The new approach is then evaluated against traditional apparent rate (AR) and Equilibrium‐kinetic (EK) methods. Our results reveal that the new approach effectively identifies fast species and simplifies reaction networks, thus significantly reducing stiffness and computational costs while maintaining remarkable accuracy. Overall, our approach offers a robust and efficient solution for modeling stiff biogeochemical processes in groundwater systems. Its successful application to diverse reaction networks highlights its potential for broad implementation in environmental and engineering contexts, paving the way for accurate and computationally feasible groundwater quality assessments.
Impact of the Indian Ocean Dipole Mode on Planetary Boundary Layer Ozone in China
Authors: Jiang Zhongjing , Li Jing , Liu Guanyu , Zhang Chongzhao 
Published: October 2024

AbstractThe Indian Ocean Dipole (IOD) mode exerts distinct impacts on the climate in China and can further affect tropospheric ozone. Using long‐term GEOS‐Chem simulations, we found distinct changes in planetary boundary layer ozone throughout China during positive and negative phases of IOD. In summer, ozone shows synchronized increases except in southern China during positive IOD; the ozone increases are dominated by chemical production and transport in northern and western China, respectively. The increased precursor from biogenic emissions contributed to ozone chemical formation in the northern region, and the increased precipitation and decreased solar radiation hindered ozone production in southern China. Ozone changes show good symmetry over most regions during negative IOD. In autumn, the ozone reduction in southern China shares the same reason as summer, while the chemical increase over northern China is affected more by changes in solar radiation and relative humidity than in the precursor emission.
Scale‐Dependent Drivers of Air‐Sea CO2 Flux Variability
Authors: Fay Amanda R. , Carroll Dustin , McKinley Galen A. , Menemenlis Dimitris , Zhang Hong 
Published: October 2024

AbstractIn climate studies, it is crucial to distinguish between changes caused by natural variability and those resulting from external forcing. Here we use a suite of numerical experiments based on the ECCO‐Darwin ocean biogeochemistry model to separate the impact of the atmospheric carbon dioxide (CO2) growth rate and climate on the ocean carbon sink — with a goal of disentangling the space‐time variability of the dominant drivers. When globally integrated, the variable atmospheric growth rate and climate exhibit similar magnitude impacts on ocean carbon uptake. At local scales, interannual variability in air‐sea CO2 flux is dominated by climate. The implications of our study for real‐world ocean observing systems are clear: in order to detect future changes in the ocean sink due to slowing atmospheric CO2 growth rates, better observing systems and constraints on climate‐driven ocean variability are required.
An Adaptive X‐Ray Dynamic Image Estimation Method Based on OMNI Solar Wind Parameters and SXI Simulated Observations
Authors: Wang R. C. , Jorgensen Anders M. , Li Dalin , Sun Tianran , Yang Zhen , Peng Xiaodong 
Published: October 2024

AbstractObservations of the overall interactions between solar wind and the Earth's magnetosphere are crucial for space weather monitoring. Upcoming missions like the Solar Wind Magnetosphere Ionosphere Link Explorer (SMILE) and the Lunar Environment heliosphere X‐ray Imager (LEXI) aim to make comprehensive global imaging of Earth's magnetosphere using soft X‐ray imager (SXI) in order to understand its dynamic response to solar wind impact. Short‐duration X‐ray images have a low signal‐to‐noise ratio (SNR), limited by cosmic background and Poisson noise. Longer integration times provide better SNR of magnetospheric structures but fail to capture the short‐term dynamics during the integration. Our study introduces a neural network method which is able to estimate the short‐term dynamics during a long integration, driven by OMNI solar wind data and simulated soft X‐ray images. Specifically, an adaptive X‐ray image estimator and a spatio‐temporal discriminator are used. It leverages X‐ray models like Magnetohydrodynamic (MHD) and Jorgensen & Sun model, driven by OMNI data to provide high‐temporal‐resolution prior information on magnetosphere motion, with SXI observation images acting as a posterior constraint on the magnetosphere's state. Experimental validation demonstrates apparent improvements in Peak signal‐to‐noise ratio (PSNR) and Structural Similarity (SSIM) compared to traditional linear and optical flow interpolation methods. The method's flexibility, considering input‐output consistency, enables easy extension to any interval (>3 min), meeting diverse application needs. In conclusion, our study presents a new approach to soft X‐ray image estimation based on neural networks, providing insights into magnetospheric dynamics as observed in soft X‐rays.
Trends and Drivers of Water Temperature Extremes in Mountain Rivers
Authors: van Hamel A. , Brunner M. I. 
Published: October 2024

AbstractWater temperature extremes can pose serious threats to the aquatic ecosystems of mountain rivers. These rivers are influenced by snow and glaciermelt, which change with climate. As a result, the frequency and severity of water temperature extremes may change. While previous studies have documented changes in non‐extreme water temperature, it is yet unclear how extreme water temperatures change in a warming climate and how their hydro‐meteorological drivers differ from those of non‐extremes. This study aims to assess temporal changes and spatial variability in water temperature extremes and enhance our understanding of the driving processes across European mountain rivers in the current climate, at both a regional and continental scale. First, we describe the characteristics of extreme events and explore their relationships with catchment characteristics. Second, we assess trends in water temperature extremes and compare them with trends in mean water temperature. Third, we use random forest models to identify the main driving processes of water temperature extremes. Last, we conduct a co‐occurrence analysis to examine the relationship between water temperature extremes and hydro‐climatic extremes. Our results show that mean water temperature has increased by °C per decade, leading to more extreme events at high elevations in spring and summer. While non‐extreme water temperatures are mainly driven by air temperature, water temperature extremes are also importantly influenced by soil moisture, baseflow, and meltwater. Our study highlights the complexity of water temperature dynamics in mountain rivers at the regional and continental scale, especially during water temperature extremes.
Ultrastructural investigation of the posterior hyaloid membrane in posterior vitreous detachment
Authors: Nixon Thomas R. W. , Hayes Matthew J. , Snead David , Snead Martin P. 
Published: October 2024

Abstract

Background
Separation of the posterior hyaloid membrane (PHM) from the retina in posterior vitreous detachment (PVD) is a fundamental, but poorly understood, process underlying vitreoretinal disorders including retinal detachment and macular hole. We performed electron microscopy studies of the PHM after PVD to investigate its ultrastructure, associated cellular structures and relationship to the internal limiting membrane (ILM).

Methods
Post-mortem human eyes were collected from recently deceased patients over 70 years of age. A posterior scleral button was trephined to identify PVD status, and the PHM and vitreous prepared for analysis with transmission and scanning electron microscopy.

Results
Twelve eyes from six patients were collected. Seven eyes had PVD; five eyes had attached vitreous. PHM was isolated from seven of seven eyes with PVD. The PHM in eyes with PVD is a laminar lacy sheet, distinct from the disorganised fibres of vitreous gel. Eyes without PVD had vitreous encased in internal limiting membrane which had separated en bloc from the retina. Cells embedded in the PHM (laminocytes) were identified in five of seven eyes with PVD, with strands stretching into the membrane.

Conclusions
PHM isolated from eyes with PVD is distinct from artefactual separation of the ILM from the retina during dissection. PHM is ultrastructurally distinct from vitreous gel and is a separate entity. The en face appearance of PHM is similar to that of ILM, suggesting that in PVD, PHM forms from separation of an inner layer of ILM. Laminocytes may play a role in the pathogenesis of vitreoretinal disease.
Long-term carbon storage in shelf sea sediments reduced by intensive bottom trawling
Authors: Zhang Wenyan , Porz Lucas , Yilmaz Rümeysa , Wallmann Klaus , Spiegel Timo , Neumann Andreas , Holtappels Moritz , Kasten Sabine , Kuhlmann Jannis , Ziebarth Nadja , Taylor Bettina , Ho-Hagemann Ha Thi Minh , Bockelmann Frank-Detlef , Daewel Ute , Bernhardt Lea , Schrum Corinna 
Published: October 2024

AbstractBottom trawling represents the most widespread anthropogenic physical disturbance to seafloor sediments on continental shelves. While trawling-induced changes to benthic ecology have been widely recognized, the impacts on long-term organic carbon storage in marine sediments remains uncertain. Here we combined datasets of sediment and bottom trawling for a heavily trawled region, the North Sea, to explore their potential mutual dependency. A pattern emerges when comparing the surface sediment organic carbon-to-mud ratio with the trawling intensity represented by the multi-year averaged swept area ratio. The organic carbon-to-mud ratio exhibits a systematic response to trawling where the swept area ratio is larger than 1 yr−1. Three-dimensional physical–biogeochemical simulation results suggest that the observed pattern is attributed to the correlated dynamics of mud and organic carbon during transport and redeposition in response to trawling. Both gain and loss of sedimentary organic carbon may occur in weakly trawled areas, whereas a net reduction of sedimentary organic carbon is found in intensely trawled grounds. Cessation of trawling allows restoration of sedimentary carbon stock and benthic biomass, but their recovery occurs at different timescales. Our results point out a need for management of intensely trawled grounds to enhance the CO2 sequestration capacity in shelf seas.
Medical image segmentation with UNet-based multi-scale context fusion
Authors: Yuan Yongqi , Cheng Yong 
Published: October 2024

AbstractHistopathological examination holds a crucial role in cancer grading and serves as a significant reference for devising individualized patient treatment plans in clinical practice. Nevertheless, the distinctive features of numerous histopathological image targets frequently contribute to suboptimal segmentation performance. In this paper, we propose a UNet-based multi-scale context fusion algorithm for medical image segmentation, which extracts rich contextual information by extracting semantic information at different encoding stages and assigns different weights to the semantic information at different scales through TBSFF module to improve the learning ability of the network for features. Through multi-scale context fusion and feature selection networks, richer semantic features and detailed information are extracted. The target can be more accurately segmented without significantly increasing the extra overhead. The results demonstrate that our algorithm achieves superior Dice and IoU scores with a relatively small parameter count. Specifically, on the GlaS dataset, the Dice score is 90.56, and IoU is 83.47. For the MoNuSeg dataset, the Dice score is 79.07, and IoU is 65.98.
Leveraging ML for profiling lipidomic alterations in breast cancer tissues: a methodological perspective
Authors: Shahnazari Parisa , Kavousi Kaveh , Minuchehr Zarrin , Goliaei Bahram , Salek Reza M 
Published: October 2024

AbstractIn this study, a comprehensive methodology combining machine learning and statistical analysis was employed to investigate alterations in the metabolite profiles, including lipids, of breast cancer tissues and their subtypes. By integrating biological and machine learning feature selection techniques, along with univariate and multivariate analyses, a notable lipid signature was identified in breast cancer tissues. The results revealed elevated levels of saturated and monounsaturated phospholipids in breast cancer tissues, consistent with external validation findings. Additionally, lipidomics analysis in both the original and validation datasets indicated lower levels of most triacylglycerols compared to non-cancerous tissues, suggesting potential alterations in lipid storage and metabolism within cancer cells. Analysis of cancer subtypes revealed that levels of PC 30:0 were relatively reduced in HER2(−) samples that were ER(+) and PR(+) compared to those that were ER(−) and PR(−). Conversely, HER2(+) tumors, which were ER(−) and PR(−), exhibited increased concentrations of PC 30:0. This increase could potentially be linked to the role of Stearoyl-CoA-Desaturase 1 in breast cancer. Comprehensive metabolomic analyses of breast cancer can offer crucial insights into cancer development, aiding in early detection and treatment evaluation of this devastating disease.
Adversarial robustness improvement for X-ray bone segmentation using synthetic data created from computed tomography scans
Authors: Fok Wai Yan Ryana , Fieselmann Andreas , Huemmer Christian , Biniazan Ramyar , Beister Marcel , Geiger Bernhard , Kappler Steffen , Saalfeld Sylvia 
Published: October 2024

AbstractDeep learning-based image analysis offers great potential in clinical practice. However, it faces mainly two challenges: scarcity of large-scale annotated clinical data for training and susceptibility to adversarial data in inference. As an example, an artificial intelligence (AI) system could check patient positioning, by segmenting and evaluating relative positions of anatomical structures in medical images. Nevertheless, data to train such AI system might be highly imbalanced with mostly well-positioned images being available. Thus, we propose the use of synthetic X-ray images and annotation masks forward projected from 3D photon-counting CT volumes to create realistic non-optimally positioned X-ray images for training. An open-source model (TotalSegmentator) was used to annotate the clavicles in 3D CT volumes. We evaluated model robustness with respect to the internal (simulated) patient rotation $$\alpha$$
α
on real-data-trained models and real&synthetic-data-trained models. Our results showed that real&synthetic- data-trained models have Dice score percentage improvements of 3% to 15% across different $$\alpha$$
α
groups compared to the real-data-trained model. Therefore, we demonstrated that synthetic data could be supplementary used to train and enrich heavily underrepresented conditions to increase model robustness.
Critical assessment of anti-amyloid-β monoclonal antibodies effects in Alzheimer’s disease: a systematic review and meta-analysis highlighting target engagement and clinical meaningfulness
Authors: Avgerinos Konstantinos I. , Manolopoulos Apostolos , Ferrucci Luigi , Kapogiannis Dimitrios 
Published: October 2024

AbstractDespite most monoclonal antibodies against Aβ in Alzheimer’s failed to demonstrate efficacy, the newest antibodies showed statistically significant clinical effects. We conducted a systematic review and meta-analysis to assess the efficacy, target engagement, and safety of anti-Aβ antibodies in sporadic AD including phase III RCTs published up to November 28, 2023. Antibodies as a drug class, attenuated worsening on the clinical scales CDR-SB and ADAS-Cog by very small effect sizes and reduced amyloid on PET by a very large effect size. Reduction of amyloid on PET was moderately correlated with CDR-SB and ADAS-Cog reductions. However, antibodies increased risk of ARIA-E and ARIA-H by a very large and moderate effect size, respectively. In subgroup analyses by individual drug, Donanemab and Lecanemab induced the largest benefits. In subgroup analyses by binding affinity, antibodies without binding to monomers were associated with the most favorable effects. Despite statistical significance for improvement on clinical measures, antibody effects were below the threshold of clinically meaningful change during the period they were studied. However, the newest antibodies demonstrably interfere with the underlying ΑD pathophysiology and therefore their benefit could be cumulative over time leading to larger clinical effects in subsequent years. PROSPERO registration no. CRD42022381334.
In-silico and in-vitro study of novel antimicrobial peptide AM1 from Aegle marmelos against drug-resistant Staphylococcus aureus
Authors: Awdhesh Kumar Mishra Rudra , Kodiveri Muthukaliannan Gothandam 
Published: October 2024

AbstractAntimicrobial peptides have garnered increasing attention as potential alternatives due to their broad-spectrum antimicrobial activity and low propensity for developing resistance. This is for the first time; proteome sequences of Aegle marmelos were subjected to in-silico digestion and AMP prediction were performed using DBAASP server. After screening the peptides on the basis of different physiochemical property, peptide sequence GKEAATKAIKEWGQPKSKITH (AM1) shows the maximum binding affinity with − 10.2 Kcal/mol in comparison with the standard drug (Trimethoprim) with − 7.4 kcal/mol and − 6.8 Kcal/mol for DHFR and SaTrmK enzyme respectively. Molecular dynamics simulation performed for 300ns, it has been found that peptide was able to stabilize the protein more effectively, analysed by RMSD, RMSF, and other statistical analysis. Free binding energy for DHFR and SaTrmK interaction from MMPBSA analysis with peptide was found to be -47.69 and − 44.32 Kcal/mol and for Trimethoprim to be -13.85 Kcal/mol and − 11.67 Kcal/mol respectively. Further in-vitro study was performed against Methicillin Susceptible Staphylococcus aureus (MSSA), Methicillin Resistant Staphylococcus aureus (MRSA), Multi-Drug Resistant Staphylococcus aureus (MDR-SA) strain, where MIC values found to be 2, 4, and 8.5 µg/ml lesser in comparison to trimethoprim which has higher MIC values 2.5, 5, and 9.5 µg/ml respectively. Thus, our study provides the insight for the further in-vivo study of the peptides against multi-drug resistant S. aureus.