bluuebunny/crossref_metadata_2025_split huggingface dataset:
CLSY docking to Pol IV requires a conserved domain critical for small RNA biogenesis and transposon silencing
Authors: Felgines Luisa , Rymen Bart , Martins Laura M. , Xu Guanghui , Matteoli Calvin , Himber Christophe , Zhou Ming , Eis Josh , Coruh Ceyda , Böhrer Marcel , Kuhn Lauriane , Chicher Johana , Pandey Vijaya , Hammann Philippe , Wohlschlegel James , Waltz Florent , Law Julie A. , Blevins Todd 
Published: November 2024

AbstractEukaryotes must balance the need for gene transcription by RNA polymerase II (Pol II) against the danger of mutations caused by transposable element (TE) proliferation. In plants, these gene expression and TE silencing activities are divided between different RNA polymerases. Specifically, RNA polymerase IV (Pol IV), which evolved from Pol II, transcribes TEs to generate small interfering RNAs (siRNAs) that guide DNA methylation and block TE transcription by Pol II. While the Pol IV complex is recruited to TEs via SNF2-like CLASSY (CLSY) proteins, how Pol IV partners with the CLSYs remains unknown. Here, we identified a conserved CYC-YPMF motif that is specific to Pol IV and is positioned on the complex exterior. Furthermore, we found that this motif is essential for the co-purification of all four CLSYs with Pol IV, but that only one CLSY is present in any given Pol IV complex. These findings support a “one CLSY per Pol IV” model where the CYC-YPMF motif acts as a CLSY-docking site. Indeed, mutations in and around this motif phenocopy pol iv null and clsy quadruple mutants. Together, these findings provide structural and functional insights into a critical protein feature that distinguishes Pol IV from other RNA polymerases, allowing it to promote genome stability by targeting TEs for silencing.
Near-field acoustic imaging with a caged bubble
Authors: Bouchet Dorian , Stephan Olivier , Dollet Benjamin , Marmottant Philippe , Bossy Emmanuel 
Published: November 2024

AbstractBubbles are ubiquitous in many research applications ranging from ultrasound imaging and drug delivery to the understanding of volcanic eruptions and water circulation in vascular plants. From an acoustic perspective, bubbles are resonant scatterers with remarkable properties, including a large scattering cross-section and strongly sub-wavelength dimensions. While it is known that the resonance properties of bubbles depend on their local environment, it remains challenging to probe this interaction at the single-bubble level due to the difficulty of manipulating a single resonating bubble in a liquid. Here, we confine a cubic bubble inside a cage using 3D printing technology, and we use this bubble as a local probe to perform scanning near-field acoustic microscopy—an acoustic analog of scanning near-field optical microscopy. By probing the acoustic interaction between a single resonating bubble and its local environment, we demonstrate near-field imaging of complex structures with a resolution that is two orders of magnitudes smaller than the wavelength of the acoustic field. As a potential application, our approach paves the way for the development of low-cost acoustic microscopes based on caged bubbles.
Multimorbidity is associated with myocardial DNA damage, nucleolar stress, dysregulated energy metabolism, and senescence in cardiovascular disease
Authors: Tomkova Kristina , Roman Marius , Adebayo Adewale S. , Sheikh Sophia , Yusoff Syabira , Gulston Melanie , Joel-David Lathishia , Lai Florence Y. , Murgia Antonio , Eagle-Hemming Bryony , Aujla Hardeep , Chad Tom , Richardson Gavin D. , Griffin Julian L. , Murphy Gavin J. , Woźniak Marcin J. 
Published: November 2024

AbstractThis study investigates why individuals with multimorbidity—two or more chronic conditions—are more prone to adverse outcomes after surgery. In our cohort, ninety-eight of 144 participants had multimorbidity. The myocardial transcriptome and metabolites involved in energy production were measured in 53 and 57 sequential participants, respectively. Untargeted analysis of the metabolome in blood and myocardium was performed in 30 sequential participants. Mitochondrial respiration in circulating mononuclear cells was measured in 70 participants. Results highlighted four main biological processes associated with multimorbidity: DNA damage with epigenetic changes, mitochondrial energy disruption, cellular aging (senescence) and innate immune response. Histone 2B, its ubiquitination enzymes and AKT3 were upregulated in the multimorbid group. Plasma senescence-associated proteins (IL-1β, GM-CSF) increased with more comorbidities. DNA damage and nucleolar instability were specifically apparent in multimorbid myocardium. We conclude that multimorbidity in cardiovascular patients accelerates biological aging, making them more vulnerable to metabolic stress.
Native learning ability and not age determines the effects of brain stimulation
Authors: Maceira-Elvira Pablo , Popa Traian , Schmid Anne-Christine , Cadic-Melchior Andéol , Müller Henning , Schaer Roger , Cohen Leonardo G. , Hummel Friedhelm C. 
Published: November 2024

AbstractHealthy aging often entails a decline in cognitive and motor functions, affecting independence and quality of life in older adults. Brain stimulation shows potential to enhance these functions, but studies show variable effects. Previous studies have tried to identify responders and non-responders through correlations between behavioral change and baseline parameters, but results lack generalization to independent cohorts. We propose a method to predict an individual’s likelihood of benefiting from stimulation, based on baseline performance of a sequential motor task. Our results show that individuals with less efficient learning mechanisms benefit from stimulation, while those with optimal learning strategies experience none or even detrimental effects. This differential effect, first identified in a public dataset and replicated here in an independent cohort, was linked to one’s ability to integrate task-relevant information and not age. This study constitutes a further step towards personalized clinical-translational interventions based on brain stimulation.
Large language models surpass human experts in predicting neuroscience results
Authors: Luo Xiaoliang , Rechardt Akilles , Sun Guangzhi , Nejad Kevin K. , Yáñez Felipe , Yilmaz Bati , Lee Kangjoo , Cohen Alexandra O. , Borghesani Valentina , Pashkov Anton , Marinazzo Daniele , Nicholas Jonathan , Salatiello Alessandro , Sucholutsky Ilia , Minervini Pasquale , Razavi Sepehr , Rocca Roberta , Yusifov Elkhan , Okalova Tereza , Gu Nianlong , Ferianc Martin , Khona Mikail , Patil Kaustubh R. , Lee Pui-Shee , Mata Rui , Myers Nicholas E. , Bizley Jennifer K. , Musslick Sebastian , Bilgin Isil Poyraz , Niso Guiomar , Ales Justin M. , Gaebler Michael , Ratan Murty N. Apurva , Loued-Khenissi Leyla , Behler Anna , Hall Chloe M. , Dafflon Jessica , Bao Sherry Dongqi , Love Bradley C. 
Published: November 2024

Abstract
Scientific discoveries often hinge on synthesizing decades of research, a task that potentially outstrips human information processing capacities. Large language models (LLMs) offer a solution. LLMs trained on the vast scientific literature could potentially integrate noisy yet interrelated findings to forecast novel results better than human experts. Here, to evaluate this possibility, we created BrainBench, a forward-looking benchmark for predicting neuroscience results. We find that LLMs surpass experts in predicting experimental outcomes. BrainGPT, an LLM we tuned on the neuroscience literature, performed better yet. Like human experts, when LLMs indicated high confidence in their predictions, their responses were more likely to be correct, which presages a future where LLMs assist humans in making discoveries. Our approach is not neuroscience specific and is transferable to other knowledge-intensive endeavours.
Soil microbiomes show consistent and predictable responses to extreme events
Authors: Knight Christopher G. , Nicolitch Océane , Griffiths Rob I. , Goodall Tim , Jones Briony , Weser Carolin , Langridge Holly , Davison John , Dellavalle Ariane , Eisenhauer Nico , Gongalsky Konstantin B. , Hector Andrew , Jardine Emma , Kardol Paul , Maestre Fernando T. , Schädler Martin , Semchenko Marina , Stevens Carly , Tsiafouli Maria Α. , Vilhelmsson Oddur , Wanek Wolfgang , de Vries Franciska T. 
Published: November 2024

AbstractIncreasing extreme climatic events threaten the functioning of terrestrial ecosystems1,2. Because soil microbes govern key biogeochemical processes, understanding their response to climate extremes is crucial in predicting the consequences for ecosystem functioning3,4. Here we subjected soils from 30 grasslands across Europe to four contrasting extreme climatic events under common controlled conditions (drought, flood, freezing and heat), and compared the response of soil microbial communities and their functioning with those of undisturbed soils. Soil microbiomes exhibited a small, but highly consistent and phylogenetically conserved, response under the imposed extreme events. Heat treatment most strongly impacted soil microbiomes, enhancing dormancy and sporulation genes and decreasing metabolic versatility. Microbiome response to heat in particular could be predicted by local climatic conditions and soil properties, with soils that do not normally experience the extreme conditions being imposed being most vulnerable. Our results suggest that soil microbiomes from different climates share unified responses to extreme climatic events, but that predicting the extent of community change may require knowledge of the local microbiome. These findings advance our understanding of soil microbial responses to extreme events, and provide a first step for making general predictions about the impact of extreme climatic events on soil functioning.
Microplastics in sea ice drifted to the Shiretoko Peninsula, the southern end of the Sea of Okhotsk
Authors: Ohno Hiroshi , Iizuka Yoshinori 
Published: November 2024

AbstractSea ice is regarded as a temporal sink and carrier of microplastics (MPs). Nevertheless, knowledge and understanding of MPs in sea ice remain sparse. This study investigated the abundance, composition, size (> 30 μm), and shape of MPs in four sea-ice cores retrieved at the southern end of the Sea of Okhotsk. Nine microplastic (MP) types, mostly with fragmentary shapes, were detected among ice-core sections. Most fragmentary MPs were smaller than 120 μm, but all fiber MPs were in the largest size class (> 210 μm). MP concentrations were 0–60 particles/L, with an average of 21 particles/L. Higher occurrences of MPs observed in the lower ice layers are attributable to heavier MP contamination in the southern part of the sea and/or relocation of MPs in the ice matrix. No significant correlation was found between the distributions of MP and inorganic particle (sediment) abundances, implying difference in their kinetics of suspension freezing. Taken together, these findings suggest important implications for elucidating the nature and distribution of MPs in sea ice.
Comparative study of quantitative identification methods for peri-urban areas based on a multi-indicator system
Authors: Shi Zhen , Liu Manshu , Wang Ying , Kovács Krisztina Filepné 
Published: November 2024

AbstractAs peri-urbanisation becomes widespread, quantitative methods for identifying peri-urban areas (PUAs) are increasingly abundant. This study aims to quantitatively compare the differences in the results obtained from multiple PUA identification methods within the same study area and to analyse the spatial patterns of the PUAs. The Threshold Method, Breakpoint Clustering, and Multilayer Perceptron were chosen to compare the identification results of PUAs in Zhengzhou City, China. The results show that the Threshold method identified the most PUAs but with the lowest accuracy. The accuracy of the Breakpoint Clustering was slightly lower than that of the Multilayer Perceptron. Regarding spatial layout, these identification results all show that the PUAs in the northeastern plains of Zhengzhou are more numerous than in the southwestern mountainous regions. Moreover, PUAs can generally be categorised into ring-shaped, belt-shaped, and patch-shaped forms. The spatial evolution of PUAs is influenced by urban expansion, geographical factors, and urban planning, and it can also provide timely reflections on urban development dynamics. These findings can provide valuable references for future research in selecting PUA identification methods, promoting in-depth longitudinal studies and cross-regional research, and enhancing the attention given to PUAs in urban planning and policy-making.
Unravelling the role of mitochondrial DNA in hybrid incompatibility within species of the Anopheles gambiae complex
Authors: Kriezis Antonios , Vitale Matteo , Morselli Giulia , Crisanti Andrea , Bernardini Federica 
Published: November 2024

AbstractIsolation mechanisms between mosquito species of the Anopheles gambiae complex, which includes major malaria vectors, remain poorly understood. In some cases, pre-zygotic barriers have been shown to limit gene flow between species of the complex, leading to a low level of hybridisation in nature. Post-zygotic mechanisms manifest with F1 hybrid males fully sterile and F1 hybrid females with reduced fertility. Genetic approaches combined with DNA sequencing techniques have highlighted the involvement of genomic regions in hybrid incompatibility with a predominant role of the X chromosome. In addition, differences in the phenotype of F1 hybrid males have been identified depending on the directionality of the parental cross used to generate them. All these studies have focused on the interaction of nuclear DNA elements in hybrid individuals. Given the role that mitochondrial DNA plays in genetic incompatibilities within other organisms and its unique inheritance pattern, commonly maternal, we conducted a genetic study that relied on the introgression of mitochondrial DNA between Anopheles gambiae and Anopheles arabiensis. The findings indicate that the mitochondrial switch does not appear to restore the fertility of F1 hybrid males, suggesting that mitochondrial DNA may not play a role in hybrid incompatibilities in these Anopheles species.
Enhanced operation of PVWPS based on advanced soft computing optimization techniques
Authors: Elymany Mahmoud M. , Enany Mohamed A. , Metwally Hamid , Shaier Ahmed A. 
Published: November 2024

AbstractThis study introduces three soft computing (SC) optimization algorithms aimed at enhancing the efficiency of photovoltaic water pumping systems (PVWPS). These algorithms include the Gorilla Troop Algorithm (GTO), Honey Badger Algorithm (HBA), and Snake Algorithm (SAO). The goal of the SC optimizers is to maximize the output power of the PV array (PPV) and enhance the efficiency of the DC motor (η), thereby optimizing the water flow rate (Q) of the pumping system. The analytical modeling approach proposed in this study involves forecasting the optimal duty cycle (Dop) for a buck-boost converter, taking into account variables such as solar radiation (G) and ambient temperature (T). A comparative analysis is conducted between the suggested SC optimizers and analytical modeling. MATLAB simulation is employed to explore an adaptive neuro-fuzzy inference system (ANFIS) trained for the proposed system. The objective is to assess system performance and accuracy. Findings indicate a strong convergence between the analytical model and the simulation model utilizing SC optimizers. Moreover, the neuro-fuzzy system trained offline, coupled with the proposed SC optimizers, demonstrates superior performance compared to traditional control methods like perturb and observe (P&O) and incremental conductance (IC). This superiority is evident across various metrics including motor efficiency (η), photovoltaic (PV) output power (PPV), water flow rate (Q), and time response.
Integrating artificial neural networks, multi-objective metaheuristic optimization, and multi-criteria decision-making for improving MXene-based ionanofluids applicable in PV/T solar systems
Authors: Hai Tao , Basem Ali , Alizadeh As’ad , Sharma Kamal , jasim Dheyaa J. , Rajab Husam , Mabrouk Abdelkader , Kolsi Lioua , Rajhi Wajdi , Maleki Hamid , Sawaran Singh Narinderjit Singh 
Published: November 2024

AbstractOptimization of thermophysical properties (TPPs) of MXene-based nanofluids is essential to increase the performance of hybrid solar photovoltaic and thermal (PV/T) systems. This study proposes a hybrid approach to optimize the TPPs of MXene-based Ionanofluids. The input variables are the MXene mass fraction (MF) and temperature. The optimization objectives include three TPPs: specific heat capacity (SHC), dynamic viscosity (DV), and thermal conductivity (TC). In the proposed hybrid approach, the powerful group method of data handling (GMDH)-type ANN technique is used to model TPPs in terms of input variables. The obtained models are integrated into the multi-objective particle swarm optimization (MOPSO) and multi-objective thermal exchange optimization (MOTEO) algorithms, forming a three-objective optimization problem. In the final step, the TOPSIS technique, one of the well-known multi-criteria decision-making (MCDM) approaches, is employed to identify the desirable Pareto points. Modeling results showed that the developed models for TC, DV, and SHC demonstrate a strong performance by R-values of 0.9984, 0.9985, and 0.9987, respectively. The outputs of MOPSO revealed that the Pareto points dispersed a broad range of MXene MFs (0-0.4%). However, the temperature of these optimal points was found to be constrained within a narrow range near the maximum value (75 °C). In scenarios where TC precedes other objectives, the TOPSIS method recommended utilizing an MF of over 0.2%. Alternatively, when DV holds greater importance, decision-makers can opt for an MF ranging from 0.15 to 0.17%. Also, when SHC becomes the primary concern, TOPSIS advised utilizing the base fluid without any MXene additive.
Exposure of gut bacterial isolates to the anthelminthic drugs, ivermectin and moxidectin, leads to antibiotic-like phenotypes of growth inhibition and adaptation
Authors: Dommann Julian , Keiser Jennifer , Garneau Julian , Gandelin Alison , Casanova Carlo , Keller Peter M. , Sayasone Somphou , Vonaesch Pascale , Schneeberger Pierre H. H. 
Published: November 2024

AbstractDue to their broad-spectrum activities, ivermectin and moxidectin are widely used anthelminthics in veterinary and human medicine. However, ivermectin has recently been shown to perturbate bacterial growth. Given the macrolide-like structure of both ivermectin and moxidectin, there is a need to characterize the antibiotic spectrum of these anthelminthics and their potential implications in the development of cross-resistance to macrolides and other families of antibiotics. Here, we characterize growth dynamics of 59 bacterial isolates in presence of ivermectin and moxidectin. Further, we assessed the effect of repeated anthelminthic exposure in 5 bacterial isolates on sensitivity to different antibiotics, both via growth dynamics and minimal inhibitory concentration. We found, that anthelminthic growth phenotypes are comparable to a selection of tested antibiotics. Bacterial anthelminthic challenging resulted in decreased anthelminthic sensitivity, and to some extent, decreased antibiotic sensitivity. Hence, potential off-target effects of large-scale administration of ivermectin and moxidectin should be carefully monitored.
Revolutionising agri‐energy: A comprehensive survey on the applications of artificial intelligence in agricultural energy internet
Authors: Fu Xueqian , Ye Wei , Li Xin , Zeng Xiangrong , Wang Yubo , Chang Fuhao , Zhang Jing , Liu Ruihan 
Published: November 2024

AbstractConsidering the rapid advancements in AI technologies such as reinforcement learning, ChatGPT, and deep learning, this paper conducts a comprehensive survey of the technological landscape of AI in the energy and agriculture sectors. It delineates the evolutionary path of AI technologies in smart grids and precision agriculture, highlighting significant advancements in energy prediction, optimisation of production and consumption, and intelligent management. Furthermore, the paper identifies key AI technologies crucial for the Agricultural Energy Internet (AEI), offering specialised exploration into AI applications for crop cultivation and fisheries, including disease detection, yield prediction, and resource management. The research provides essential theoretical foundations for AI integration in each of these agricultural domains. In addition, the paper envisions the future integration of ChatGPT in coupled modelling of agriculture and energy systems, enhancing synergistic intelligent control, and AI‐driven carbon tracking technologies within the AEI. This study facilitates a greater grasp of the transformative potential of AI in reshaping the nexus of agriculture and energy.
Overvoltage ride through control strategy for improving voltage support capability of virtual synchronous generator
Authors: Zhao Xinmin , Zhang Haibo , Ndonji Joseph , Jiang Weiyong , Li Kai 
Published: November 2024

AbstractAs the application of renewable energy sources continues to increase, the virtual synchronous generator (VSG) has been proposed and received widespread attention. In China, new energy stations are typically situated at the transmission end of LCC‐HVDC lines. A failure in DC commutation can lead to overvoltage at the DC transmission end, thus posing a significant risk of disconnection from the grid due to the occurrence of overvoltage. Compared to when low‐voltage occur, it was discovered that, during overvoltage, VSGs face difficulties in rapidly attaining the preset power and transient instability. These issues can significantly impact the voltage support capability of the VSG. To address these concerns, an enhanced control strategy has been proposed. The control strategy sets reasonable power reference values, leveraging the advantage of fast voltage response in VSG by matching its voltage with virtual impedance. This ensures that the power rapidly reaches the designated reference value while reducing fluctuations in power angle. The transient power‐angle stability is validated using the energy function method. This approach enhances the reactive power absorbed by VSG, thereby mitigating transient overvoltage. Finally, simulation results from PSCAD/EMTDC validate the rationale and effectiveness of the control strategy.