bluuebunny/crossref_metadata_2025_split huggingface dataset:
Confirmation and inheritance of glufosinate resistance in an Amaranthus palmeri population from North Carolina
Authors: Jones Eric A. L. , Dunne Jeffrey C. , Cahoon Charles W. , Jennings Katherine M. , Leon Ramon G. , Everman Wesley J. 
Published: June 2024

AbstractA putative glufosinate‐resistant Amaranthus palmeri population was reported in 2015 in Anson County, North Carolina. The results from dose–response assays conducted in the field suggested plants were surviving lethal rates of glufosinate. Dose–response assays conducted in the glasshouse determined the Anson County accession exhibited reduced susceptibility to glufosinate compared to three glufosinate‐susceptible populations. The LD50 values (210–316 g ai ha−1) for the Anson County population were always higher than the LD50 values (118–158 g ai ha−1) for the tested susceptible populations from the dose–response assays. Anson County plants that survived lethal glufosinate rates were reciprocally crossed with susceptible plants to create F1 genotypes and treated with a lethal rate of glufosinate (267 g ai ha−1; ascertained from glasshouse dose–response assay) to determine the distribution of injury and survival for each cross compared to a cross of susceptible parents. The distribution of injury was non‐normal for the crosses containing an Anson County plant compared to the cross with a susceptible parent. Survival was 68%–84% for crosses containing an Anson County plant, whereas the survival was significantly reduced to 35% for the susceptible plant cross. Chi‐square goodness of fit tests were used to test inheritance models to describe the responses of the genotypes. The resistant × susceptible crosses were best described with a heterozygous two loci with incomplete dominance model compared to the resistant × resistant cross that was best described with a heterozygous single locus with incomplete dominance model. The Anson County population has evolved resistance to glufosinate that is heritable and likely conferred by an oligogenic mechanism with incomplete dominance.
High‐Performance Multifunctional Carbon Fibrous Sponges Derived from Pitch
Authors: Wang Haiyang , Bai Xiaopeng , Wu Yufeng , Peng Du , Liu Junchen , Li Ziwei , Cheng Zekun , Zhou Yiqian , Huang Kai , Li Bo , Wu Hui 
Published: June 2024

Abstract3D carbon‐based porous sponges are recognized for significant potential in oil absorption and electromagnetic interference (EMI). However, their widespread application is hindered by a common compromise between high performance and affordability of mass production. Herein, a novel approach is introduced that involves laser‐assisted micro‐zone heating melt‐blown spinning (LMHMS) to address this challenge by creating pitch‐based submicron carbon fibers (PSCFs) sponge with 3D interconnected structures. These structures bestow the resulting sponge exceptional characteristics including low density (≈20 mg cm−3), high porosity (≈99%), remarkable compressibility (80% maximum strain), and superior conductivity (≈628 S m−1). The resultant PSCF sponges realize an oil/organic solvent sorption capacity over 56 g/g and possess remarkable regenerated ability. In addition to their effectiveness in cleaning up oil/organic solvent spills, they also demonstrated strong electromagnetic shielding capabilities, with a total shielding effectiveness (SE) exceeding 60 dB across the X‐band GHz range. In virtue of extreme lightweight of ≈20 mg cm−3, the specific SE of the PSCF sponge reaches as high as ≈1466 dB cm3 g−1, surpassing the performance of numerous carbon‐based porous structures. Thus, the unique blend of properties renders these sponges promising for transforming strategies in addressing oil/organic solvent contaminations and providing effective protection against EMI.
FinTech services from BigTech companies
Authors: Puschmann Thomas , Halimi Engjull 
Published: June 2024

AbstractThe so‐called BigTech companies Amazon, Alphabet, Apple and Meta and are constantly attempting to grow into new business areas. Due to their expertise in data analysis, they have managed to quickly establish themselves in many industries as digital ecosystems and, as part of this strategy, are also expanding their footprint in financial services. However, the biggest challenge that BigTech companies are facing in financial services is customer acceptance. This paper contributes to the emerging field of digital ecosystems and the acceptance of sensitive services by users. Although the BigTech firms have gained much attention, only little empirical analysis is available. This paper aims to shed light on the determinants of customer acceptance of BigTech banking services. Based on a survey in Switzerland this research develops an analytic model to identify and test the relevant determinants. The results indicate that the strongest significant influences were found in subjective risk and trust which clearly demonstrates the still‐existing incumbents' advantage over the BigTech companies.
Die Rolle von OPA1 bei der Fusion von Mitochondrien
Authors: von der Malsburg Alexander 
Published: June 2024

AbstractMitochondria form extensive networks that rapidly adapt to cellular demands. The formation and disassembly of these structures result from a balance between fission and fusion of the two mitochondrial membranes that are decisively controlled by Dynamin-like GTPases such as OPA1. This protein forms oligomers which mediate the fusion of the inner mitochondrial membrane by a unique mechanism that involves the generation of curvature and the extraction of cardiolipin from the lipid bilayer.
Nitrogenasen: Vielfältige Katalysatoren für eine nachhaltige Bioökonomie?
Authors: Rebelein Johannes Georg 
Published: June 2024

AbstractNitrogenases are the only enzymes that reduce molecular nitrogen (N2) to ammonia. Recently, we could demonstrate that nitrogenases also reduce the greenhouse gas carbon dioxide (CO2), suggesting CO2 to be substrate. Indeed, the iron nitrogenase reduces CO2 into formate and CH4 under physiological conditions. Based on Rhodobacter capsulatus, we could establish a light-driven formation of these products, which serve as substrates for other microbes and feedstock chemicals for a green economy.
Construction of Remote Sensing Quantitative Model for Biomass of Deciduous Broad-Leaved Forest in Mazongling Nature Reserve Based on Machine Learning
Authors: Tang Xuehai , Yu Dagui , Lv Haiyan , Ou Qiangxin , Xie Meiqin , Fan Peng , Huang Qingfeng 
Published: June 2024

AbstractAs an important forest type, deciduous broad-leaved forest is crucial for estimating forest carbon sequestration capacity and evaluating forest carbon balance. This study focuses on the natural deciduous broad-leaved forest of Mazongling Nature Reserve in Jinzhai County of China. WorldView-2 images were selected as data source. 36 candidate factors including vegetation indices, texture features, and topographic factors were used for modelling. Three machine learning algorithms (i.e., random forest, k-nearest neighbor, and artificial neural network) were used to establish the optimal quantitative retrieval model for natural deciduous broad-leaved biomass. Results showed that the ANN model was the best predictor with R2 = 0.69 and RMSE = 31.53 (Mg·ha−1). Combining the ANN model with the complete spatial coverage of remote sensing data, we developed a distribution map of natural deciduous broad-leaved biomass in the Mazongling forest farm. The estimated average biomass of the study area was 90.34 ± 47.96 Mg·ha−1. In addition, the influence of light saturation on model accuracy is also discussed. This study confirms that remote sensing data in temporal and spatial space can improve the model estimation accuracy.
GMPP-NN: a deep learning architecture for graph molecular property prediction
Authors: Abbassi Outhman , Ziti Soumia , Belhiah Meryam , Lagmiri Souad Najoua , Zaoui Seghroucheni Yassine 
Published: June 2024

AbstractThe pharmacy industry is highly focused on drug discovery and development for the identification and optimization of potential drug candidates. One of the key aspects of this process is the prediction of various molecular properties that justify their potential effectiveness in treating specific diseases. Recently, graph neural networks have gained significant attention, primarily due to their strong suitability for predicting complex relationships that exist between atoms and other molecular structures. GNNs require significant depth to capture global features and to allow the network to iteratively aggregate and propagate information across the entire graph structure. In this research study, we present a deep learning architecture known as a graph molecular property prediction neural network. which combines MPNN feature extraction with a multilayer perceptron classifier. The deep learning architecture was evaluated on four benchmark datasets, and its performance was compared to the smiles transformer, fingerprint to vector, deeper graph convolutional networks, geometry-enhanced molecular, and atom-bond transformer-based message-passing neural network. The results showed that the architecture outperformed the other models using the receiver operating characteristic area under the curve metric. These findings offer an exciting opportunity to enhance and improve molecular property prediction in drug discovery and development.
Electronic, magnetic and optical properties of CoNi spinel ferrites doped by rare earth atoms: a density functional theory study
Authors: Caliskan Serkan , Rodriguez Adelina M. , Alexander Stefan , Almessiere Munirah A. , Baykal Abdulhadi , Slimani Yassine 
Published: June 2024

AbstractDensity Functional Theory is employed to investigate how specific rare earth (RE) dopants (Pr, Y, Dy) affect the electronic, magnetic, and optical characteristics of Co0.5Ni0.5Fe2O4 (CN) spinel ferrites. The impact of RE atoms, which replace Fe ions in the supercell, is elucidated by analyzing the spin-resolved density of states, magnetic moment, and optical parameters. The findings demonstrate that both single and collective RE doping play a vital role in manipulating the key properties of these materials. Notably, we reveal that incorporating RE dopants into CN structures leads to a reduction in the energy band gap. Additionally, this work underscores substantial spin-dependent behavior and provides valuable insights into optical properties, suggesting that RE-doped CNs hold significant potential in the fields of spintronics and optoelectronics.
Assessment of the AquaCrop model to simulate the impact of soil fertility management on evapotranspiration, yield, and water productivity of maize (Zea May L.) in the sub-humid agro-ecology of Nigeria
Authors: Adeboye Omotayo B. , Schultz Bart , Adeboye Amaka P. , Chukalla Abebe , Shittu Kabiru A. 
Published: June 2024

AbstractField experiments were conducted for two seasons in Ile-Ife, Nigeria to evaluate the performance of the AquaCrop model in simulating the effects of soil fertility management on the canopy cover (CC), soil water storages (SWS), cumulative aboveground biomass (BM), evapotranspiration (ETa), grain yields, and water productivity (WP) of rainfed maize. Six levels of soil fertility management and two cultivars of maize, SUWAN 1-SR and PVA led to a 2 by 6 factorial experimental treatment and arranged in a randomized complete block design. Agronomic and environmental parameters were measured for two consecutive seasons. The AquaCrop model was calibrated using data from the wetter year. The AquaCrop model captured well the variances in the CC, R2 ≥ 0.88, RMSE ≤ 14.2, and d-index ≥ 0.97 under full and stressed soil fertility. Although the AquaCrop model over and underestimated SWS, it is still within acceptable limits. The model simulated SWS well, R2 ≥ 0.71, EF ≥ 0.97, and d-index ≥ 0.97. AquaCrop tends to underestimate ETa under rainfall and NPK variabilities. The AquaCrop model simulated grain yields excellently, R2 = 0.99, b = 1.00. The 150% of the recommended NPK application is suitable for the desired improvement in land and water productivity of the crop. The AquaCrop model predicted and captured the trends in the yields and water productivity of maize adequately under varying NPK applications. Further research is required on other cultivars of the crop and locations in the area in order to generalize the adequacy of the model.
Variability of Dissolved Organic Matter Sources in the Upper Eurasian Arctic Ocean
Authors: Kong Xianyu , Granskog Mats A. , Hoppe Clara J. M. , Fong Allison A. , Stedmon Colin A. , Tippenhauer Sandra , Ulfsbo Adam , Vredenborg Myriel , Koch Boris P. 
Published: June 2024

AbstractChromophoric dissolved organic matter (CDOM) is a ubiquitous component in marine environments, and substantial changes in its sources and distribution, related to the carbon cycle in the Arctic Ocean, are expected due to Arctic warming. In this study, we present unique CDOM data in the Eurasian Arctic Ocean derived from the year‐round MOSAiC expedition. We used CDOM absorbance spectra and fluorescence excitation‐emission matrices in combination with parallel factor analysis to characterize differences in DOM sources and composition. Our results suggested that terrestrial DOM was less sensitive to seasonal changes but controlled by regionality in hydrography. Elevated dissolved organic carbon (DOC) levels in polar surface water were primarily derived from terrigenous sources as identified by CDOM absorption and fluorescence characteristics. In the Amundsen Basin and western Fram Strait surface waters, to which terrestrial DOM is primarily transported by the Transpolar Drift, we found, on average, a 188% larger meteoric water fraction and a 40% higher DOC concentration compared to the Atlantic water that dominated western Nansen Basin and Yermak Plateau. In the Amundsen Basin, the DOC concentration in summer of surface water was only 13% higher compared to winter season. Additionally, autochthonous DOM and chlorophyll‐a concentrations were relatively low in surface water and exhibited significant differences compared to those observed in summer, while there were significant differences between autochthonous DOM and chlorophyll‐a. We also observed that sea ice melt contributed to autochthonous DOM in summer, while storms in winter affected the vertical distribution of terrestrial and autochthonous DOM in the subsurface.
NOS2-derived low levels of NO drive psoriasis pathogenesis
Authors: Köhler Ines , Bivik Eding Cecilia , Kasic Nada-Katarina , Verma Deepti , Enerbäck Charlotta 
Published: June 2024

AbstractPsoriasis is an IL-23/Th17-mediated skin disorder with a strong genetic predisposition. The impact of its susceptibility gene nitric oxide synthase 2 (NOS2) remains unknown. Here, we demonstrate strong NOS2 mRNA expression in psoriatic epidermis, an effect that is IL-17 dependent. However, its complete translation to protein is prevented by the IL-17-induced miR-31 implying marginally upregulated NO levels in psoriatic skin. We demonstrate that lower levels of NO, as opposed to higher levels, increase keratinocyte proliferation and mediate IL-17 downstream effects. We hypothesized that the psoriatic phenotype may be alleviated by either eliminating or increasing cellular NO levels. In fact, using the imiquimod psoriasis mouse model, we found a profound impact on the psoriatic inflammation in both IMQ-treated NOS2 KO mice and wild-type mice treated with IMQ and the NO-releasing berdazimer gel. In conclusion, we demonstrate that IL-17 induces NOS2 and fine-tunes its translation towards a window of proinflammatory and hyperproliferative effects and identify NO donor therapy as a new treatment modality for psoriasis.
Imputation of label-free quantitative mass spectrometry-based proteomics data using self-supervised deep learning
Authors: Webel Henry , Niu Lili , Nielsen Annelaura Bach , Locard-Paulet Marie , Mann Matthias , Jensen Lars Juhl , Rasmussen Simon 
Published: June 2024

AbstractImputation techniques provide means to replace missing measurements with a value and are used in almost all downstream analysis of mass spectrometry (MS) based proteomics data using label-free quantification (LFQ). Here we demonstrate how collaborative filtering, denoising autoencoders, and variational autoencoders can impute missing values in the context of LFQ at different levels. We applied our method, proteomics imputation modeling mass spectrometry (PIMMS), to an alcohol-related liver disease (ALD) cohort with blood plasma proteomics data available for 358 individuals. Removing 20 percent of the intensities we were able to recover 15 out of 17 significant abundant protein groups using PIMMS-VAE imputations. When analyzing the full dataset we identified 30 additional proteins (+13.2%) that were significantly differentially abundant across disease stages compared to no imputation and found that some of these were predictive of ALD progression in machine learning models. We, therefore, suggest the use of deep learning approaches for imputing missing values in MS-based proteomics on larger datasets and provide workflows for these.
Restricting datasets to classifiable samples augments discovery of immune disease biomarkers
Authors: Glehr Gunther , Riquelme Paloma , Kronenberg Katharina , Lohmayer Robert , López-Madrona Víctor J. , Kapinsky Michael , Schlitt Hans J. , Geissler Edward K. , Spang Rainer , Haferkamp Sebastian , Hutchinson James A. 
Published: June 2024

AbstractImmunological diseases are typically heterogeneous in clinical presentation, severity and response to therapy. Biomarkers of immune diseases often reflect this variability, especially compared to their regulated behaviour in health. This leads to a common difficulty that frustrates biomarker discovery and interpretation – namely, unequal dispersion of immune disease biomarker expression between patient classes necessarily limits a biomarker’s informative range. To solve this problem, we introduce dataset restriction, a procedure that splits datasets into classifiable and unclassifiable samples. Applied to synthetic flow cytometry data, restriction identifies biomarkers that are otherwise disregarded. In advanced melanoma, restriction finds biomarkers of immune-related adverse event risk after immunotherapy and enables us to build multivariate models that accurately predict immunotherapy-related hepatitis. Hence, dataset restriction augments discovery of immune disease biomarkers, increases predictive certainty for classifiable samples and improves multivariate models incorporating biomarkers with a limited informative range. This principle can be directly extended to any classification task.
IFNγ causes mitochondrial dysfunction and oxidative stress in myositis
Authors: Abad Catalina , Pinal-Fernandez Iago , Guillou Clement , Bourdenet Gwladys , Drouot Laurent , Cosette Pascal , Giannini Margherita , Debrut Lea , Jean Laetitia , Bernard Sophie , Genty Damien , Zoubairi Rachid , Remy-Jouet Isabelle , Geny Bernard , Boitard Christian , Mammen Andrew , Meyer Alain , Boyer Olivier 
Published: June 2024

AbstractIdiopathic inflammatory myopathies (IIMs) are severe autoimmune diseases with poorly understood pathogenesis and unmet medical needs. Here, we examine the role of interferon γ (IFNγ) using NOD female mice deficient in the inducible T cell co-stimulator (Icos), which have previously been shown to develop spontaneous IFNγ-driven myositis mimicking human disease. Using muscle proteomic and spatial transcriptomic analyses we reveal profound myofiber metabolic dysregulation in these mice. In addition, we report muscle mitochondrial abnormalities and oxidative stress in diseased mice. Supporting a pathogenic role for oxidative stress, treatment with a reactive oxygen species (ROS) buffer compound alleviated myositis, preserved muscle mitochondrial ultrastructure and respiration, and reduced inflammation. Mitochondrial anomalies and oxidative stress were diminished following anti-IFNγ treatment. Further transcriptomic analysis in IIMs patients and human myoblast in vitro studies supported the link between IFNγ and mitochondrial dysfunction observed in mice. These results suggest that mitochondrial dysfunction, ROS and inflammation are interconnected in a self-maintenance loop, opening perspectives for mitochondria therapy and/or ROS targeting drugs in myositis.
Predicting host range expansion in parasitic mites using a global mammalian-acarine dataset
Authors: Klimov Pavel B. , He Qixin 
Published: June 2024

AbstractMulti-host parasites pose greater health risks to wildlife, livestock, and humans than single-host parasites, yet our understanding of how ecological and biological factors influence a parasite’s host range remains limited. Here, we assemble the largest and most complete dataset on permanently parasitic mammalian mites and build a predictive model assessing the probability of single-host parasites to become multi-hosts, while accounting for potentially unobserved host-parasite links and class imbalance. This model identifies statistically significant predictors related to parasites, hosts, climate, and habitat disturbance. The most important predictors include the parasite’s contact level with the host immune system and two variables characterizing host phylogenetic similarity and spatial co-distribution. Our model reveals an overrepresentation of mites associated with Rodentia (rodents), Chiroptera (bats), and Carnivora in the multi-host risk group. This highlights both the potential vulnerability of these hosts to parasitic infestations and the risk of serving as reservoirs of parasites for new hosts. In addition, we find independent macroevolutionary evidence that supports our prediction of several single-host species of Notoedres, the bat skin parasites, to be in the multi-host risk group, demonstrating the forecasting potential of our model.
METTL3-mediated chromatin contacts promote stress granule phase separation through metabolic reprogramming during senescence
Authors: Wang Chen , Tanizawa Hideki , Hill Connor , Havas Aaron , Zhang Qiang , Liao Liping , Hao Xue , Lei Xue , Wang Lu , Nie Hao , Qi Yuan , Tian Bin , Gardini Alessandro , Kossenkov Andrew V. , Goldman Aaron , Berger Shelley L. , Noma Ken-ichi , Adams Peter D. , Zhang Rugang 
Published: June 2024

AbstractMETTL3 is the catalytic subunit of the methyltransferase complex, which mediates m6A modification to regulate gene expression. In addition, METTL3 regulates transcription in an enzymatic activity-independent manner by driving changes in high-order chromatin structure. However, how these functions of the methyltransferase complex are coordinated remains unknown. Here we show that the methyltransferase complex coordinates its enzymatic activity-dependent and independent functions to regulate cellular senescence, a state of stable cell growth arrest. Specifically, METTL3-mediated chromatin loops induce Hexokinase 2 expression through the three-dimensional chromatin organization during senescence. Elevated Hexokinase 2 expression subsequently promotes liquid-liquid phase separation, manifesting as stress granule phase separation, by driving metabolic reprogramming. This correlates with an impairment of translation of cell-cycle related mRNAs harboring polymethylated m6A sites. In summary, our results report a coordination of m6A-dependent and -independent function of the methyltransferase complex in regulating senescence through phase separation driven by metabolic reprogramming.