bluuebunny/crossref_metadata_2025_split huggingface dataset:
The complex structure of aquatic food webs emerges from a few assembly rules
Authors: García-Oliva Ovidio , Wirtz Kai 
Published: February 2025

Abstract
Food-web theory assumes that larger-bodied predators generally select larger prey. This allometric rule fails to explain a considerable fraction of trophic links in aquatic food webs. Here we show that food-web constraints result in guilds of predators that vary in size but have specialized on prey of the same size, and that the distribution of such specialist guilds explains about one-half of the food-web structure. We classified 517 pelagic species into five predator functional groups. Most of these follow three prey selection strategies: a guild following the allometric rule whereby larger predators eat larger prey and two guilds of specialists that prefer either smaller or larger prey than predicted by the allometric rule. Such coexistence of non-specialist and specialist guilds independent from taxa or body size points towards structural principles behind ecological complexity. We show that the pattern describes >90% of observed linkages in 218 food webs in 18 aquatic ecosystems worldwide. The pattern can be linked to eco-evolutionary constraints to prey exploitation and provides a blueprint for more effective food-web models.
Persistence of davemaoite at lower-mantle conditions
Authors: Wang Lin , Miyajima Nobuyoshi , Wang Fei , Katsura Tomoo 
Published: February 2025

Abstract
The lower mantle occupies over half of Earth’s volume, and accordingly, its mineralogy is crucial in determining the structure and dynamics of Earth. Davemaoite, the calcium silicate perovskite, was believed to coexist with bridgmanite in the lower mantle and is considered essential for understanding the chemical evolution and dynamics of Earth’s lower mantle. However, the presence of davemaoite is challenged due to the potential for high calcium silicate solubility in bridgmanite. Here we use an ultrahigh-pressure multi-anvil technique to show experimentally that the calcium solubility in bridgmanite is insufficient to eliminate davemaoite under mantle conditions, including typical mantle pressure, temperature and chemical compositions. We conclude that davemaoite has been stable in Earth’s lower mantle since its formation. Due to the limited calcium solubility in bridgmanite, davemaoite-enriched domains are expected at the core–mantle boundary. These domains could serve as the principal reservoir for incompatible elements in the lower mantle and may be the source for some ocean island basalts. Furthermore, our study offers an explanation for the observed large low-shear-wave-velocity provinces at the bottom of the lower mantle. These provinces may consist of davemaoite-enriched materials crystallized from basal magma ocean in early Earth history.
Rapid brain tumor classification from sparse epigenomic data
Authors: Brändl Björn , Steiger Mara , Kubelt Carolin , Rohrandt Christian , Zhu Zhihan , Evers Maximilian , Wang Gaojianyong , Schuldt Bernhard , Afflerbach Ann-Kristin , Wong Derek , Lum Amy , Halldorsson Skarphedinn , Djirackor Luna , Leske Henning , Magadeeva Svetlana , Smičius Romualdas , Quedenau Claudia , Schmidt Nils O. , Schüller Ulrich , Vik-Mo Einar O. , Proescholdt Martin , Riemenschneider Markus J. , Zadeh Gelareh , Ammerpohl Ole , Yip Stephen , Synowitz Michael , van Bömmel Alena , Kretzmer Helene , Müller Franz-Josef 
Published: February 2025

Abstract
Although the intraoperative molecular diagnosis of the approximately 100 known brain tumor entities described to date has been a goal of neuropathology for the past decade, achieving this within a clinically relevant timeframe of under 1 h after biopsy collection remains elusive. Advances in third-generation sequencing have brought this goal closer, but established machine learning techniques rely on computationally intensive methods, making them impractical for live diagnostic workflows in clinical applications. Here we present MethyLYZR, a naive Bayesian framework enabling fully tractable, live classification of cancer epigenomes. For evaluation, we used nanopore sequencing to classify over 200 brain tumor samples, including 10 sequenced in a clinical setting next to the operating room, achieving highly accurate results within 15 min of sequencing. MethyLYZR can be run in parallel with an ongoing nanopore experiment with negligible computational overhead. Therefore, the only limiting factors for even faster time to results are DNA extraction time and the nanopore sequencer’s maximum parallel throughput. Although more evidence from prospective studies is needed, our study suggests the potential applicability of MethyLYZR for live molecular classification of nervous system malignancies using nanopore sequencing not only for the neurosurgical intraoperative use case but also for other oncologic indications and the classification of tumors from cell-free DNA in liquid biopsies.
CETD, a global compound events detection and visualisation toolbox and dataset
Authors: Yin Cong , Ting Mingfang , Kornhuber Kai , Horton Radley M. , Yang Yaping , Jiang Yelin 
Published: February 2025

Abstract
Compound events (CEs) are attracting increased attention due to their significant societal and ecological impacts. However, their inherent complexity can pose challenges for climate scientists and practitioners, highlighting the need for a more approachable and intuitive framework for detecting and visualising CEs. Here, we introduce the Compound Events Toolbox and Dataset (CETD), which provides the first integrated, interactive, and extensible platform for CE detection and visualisation. Employing observations, reanalysis, and model simulations, CETD can quantify the frequency, duration, and severity of multiple CE types: multivariate, sequential, and concurrent events. It can analyse CEs often linked to severe impacts on human health, wildfires, and air pollution, such as hot-dry, wet-windy, and hot-dry-stagnation events. To validate the performance of CETD, we conduct statistical analyses for several high-impact events, such as the 2019 Australian wildfires and the 2022 European heatwaves. The accessibility and extensibility of CETD will benefit the broader community by enabling them to better understand and prepare for the risks and challenges posed by CEs in a warming world.
Experimental and numerical investigation to optimise liquid desiccant system for advanced air conditioning
Authors: Shivaprasad K. V. , Roy Sumit , Giampieri Alessandro , Smallbone Andrew , Roskilly Anthony Paul 
Published: February 2025

Abstract
This study aims to experimentally demonstrate a liquid desiccant systems effectiveness by using thermo-chemical fluid, such as aqueous solution of calcium chloride. This study evaluated the effect of operating temperatures on air properties (temperature, relative humidity, and moisture content) and system effectiveness by varying air flow rates. The system’s functionality was influenced by the operational temperature and air flow rate, and the dehumidification effectiveness was higher at low operating temperatures and low airflow rates. An ANN metamodel-based control strategy is also proposed for implementation in hybrid thermo-chemical networks with the help of system performance data and real-time data. The suggested ANN model’s results were validated using a variety of measuring techniques, including the RMSE, MAPE, correlation (R), and coefficient of determination (R2). The proposed ANN analysis achieved an excellent correlation between predicted and experimentally measured data.
Multi-modal Language models in bioacoustics with zero-shot transfer: a case study
Authors: Miao Zhongqi , Elizalde Benjamin , Deshmukh Soham , Kitzes Justin , Wang Huaming , Dodhia Rahul , Ferres Juan Lavista 
Published: February 2025

Abstract
Automatically detecting sound events with Artificial Intelligence (AI) has become increas- ingly popular in the field of bioacoustics, ecoacoustics, and soundscape ecology, particularly for wildlife monitoring and conservation. Conventional methods predominantly employ supervised learning techniques that depend on substantial amounts of manually annotated bioacoustic data. However, manual annotation in bioacoustics is tremendously resource- intensive in terms of both human labor and financial resources, and it requires considerable domain expertise. Moreover, the supervised learning framework limits the application scope to predefined categories within a closed setting. The recent advent of Multi-Modal Language Models has markedly enhanced the versatility and possibilities within the realm of AI appli- cations, as this technique addresses many of the challenges that inhibit the deployment of AI in real-world applications. In this paper, we explore the potential of Multi-Modal Language Models in the context of bioacoustics through a case study. We aim to showcase the potential and limitations of Multi-Modal Language Models in bioacoustic applications. In our case study, we applied an Audio-Language Model–—a type of Multi-Modal Language Model that aligns language with audio / sound recording data—–named CLAP (Contrastive Language–Audio Pretraining) to eight bioacoustic benchmarks covering a wide variety of sounds previously unfamiliar to the model. We demonstrate that CLAP, after simple prompt engineering, can effectively recognize group-level categories such as birds, frogs, and whales across the benchmarks without the need for specific model fine-tuning or additional training, achieving a zero-shot transfer recognition performance comparable to supervised learning baselines. Moreover, we show that CLAP has the potential to perform tasks previously unattainable with supervised bioacoustic approaches, such as estimating relative distances and discovering unknown animal species. On the other hand, we also identify limitations of CLAP, such as the model’s inability to recognize fine-grained species-level categories and the reliance on manually engineered text prompts in real-world applications.
Improving ALS detection and cognitive impairment stratification with attention-enhanced deep learning models
Authors: Xia Yuqing , Gregory Jenna M. , Waldron Fergal M. , Spence Holly , Vallejo Marta 
Published: February 2025

Abstract
Amyotrophic lateral sclerosis (ALS) is a fatal neurological disease marked by motor deterioration and cognitive decline. Early diagnosis is challenging due to the complexity of sporadic ALS and the lack of a defined risk population. In this study, we developed Miniset-DenseSENet, a convolutional neural network combining DenseNet121 with a Squeeze-and-Excitation attention mechanism, using 190 autopsy brain images from the Gregory Laboratory at the University of Aberdeen. The model distinguishes controls, ALS patients with no cognitive impairment, and ALS patients with cognitive impairment (ALS-frontotemporal dementia) with 97.37% accuracy, addressing a significant challenge in overlapping neurodegenerative disorders involving TDP-43 proteinopathy. Miniset-DenseSENet outperformed other transfer learning models, achieving a sensitivity of 1 and specificity of 0.95. These findings suggest that integrating transfer learning and attention mechanisms into neuroimaging can enhance diagnostic accuracy, enabling earlier ALS detection and improving patient stratification. This model has the potential to guide clinical decisions and support personalied therapeutic strategies.
Prebiotic formation of thioesters via cyclic anhydrides as a key step in the emergence of metabolism
Authors: El Qami Abdelkarim , Hilari Jorge Isaac , Blandin Véronique , Gayraud Oscar , Milet Anne , Vallée Yannick 
Published: February 2025

Abstract
Thioesters are high-energy derivatives of carboxylic acids that are essential in the functioning of today’s living cells. Their central role argues in favor of their early introduction in the abiotic reaction network which led to the emergence of life on Earth. We propose that the first thioesters appeared during the establishment of the reverse tricarboxylic acid (rTCA) cycle, an effective metabolic cycle for the synthesis of organic molecules from CO2. Most of the acids in this cycle are 1,4-diacids. We show that the formation of a cyclic anhydride from aqueous solutions of succinic or citric acid is possible using drying conditions over silica, as it could happen in an evaporating pond. When these 1,4-diacids are dried in the presence of thiols, thioesters are obtained. Our experimental and theoretical results demonstrate that analogs of succinyl-CoA and citryl-CoA, thioesters from the rTCA cycle, can be produced. Such a process highlights the importance of 1,4-diacids, which would have been introduced in the metabolism then under construction because of their ability to form anhydrides and to be activated in the absence of triphosphates or of any other activating agent. At its beginning, the rTCA cycle should therefore be interpreted mainly as a “1,4-diacid cycle”.
Characteristics of wetting behavior under unloading in natural granite residual soil
Authors: Yin Song , Huang Jianing , Li Xinming , Sun Yuzhou , Li Yuru , Zhang Xianwei 
Published: February 2025

Abstract
Excavation of foundation pits induces stress release in the soil, leading to deformation driven by the redistribution of internal stresses and particle adjustment. Rainfall infiltration further increases soil water content, weakening particle bonding through the dissolution of cementing agents, and inducing additional wetting deformation. However, there has only been limited experimental research examining the deformation behavior of soil under the coupled effects of unloading and wetting, especially in water-rich excavation conditions, where these factors interact dynamically. This study systematically investigates the coupled effects of unloading and wetting on the deformation behavior of natural granite residual soil (GRS) through triaxial tests. The results reveal that the interaction between unloading and wetting amplifies soil deformation, with significant non-linear dependencies on confining pressure and saturation levels. The stress–strain curves of natural GRS under unloading path exhibit strain-hardening behavior, and the vertical wetting deformation decreases with increasing saturation. Furthermore, the study identifies pronounced anisotropic wetting deformation, with tensile wetting deformation significantly exceeding compressive wetting deformation under equivalent stress states. This anisotropy diminishes with increasing confining pressure, highlighting the stress-dependency of wetting deformation behavior. The hyperbolic model shows a larger wetting deformation than the linear model, underscoring its practical significance in designing safer excavation strategies under coupled unloading and wetting conditions. These findings provide a foundation for improving deformation prediction and risk management in geotechnical engineering.
Higher relevance of mechanical determinants for short-distance performance and metabolic determinants for middle-distance performance in female adolescent swimmers at national level
Authors: Keller Sebastian , Wahl Patrick 
Published: February 2025

Abstract
The study investigated associations of metabolic, anthropometric, and neuromuscular parameters with 50 to 400 m front crawl performance. Competition performances of 24 female swimmers (14.9 ± 1.3 years) were recorded and metabolic determinants (maximal oxygen uptake and lactate accumulation [ċLamax], cost of swimming [C], and lactate threshold 1 [LT1] using 200 m all-out, 20 s sprint, 500 m submaximal, and 3 min incremental test, respectively), anthropometry and dryland strength (squat and bench press 1 repetition maximum [1RMSQ/1RMBP] and mean propulsive power [MPPSQ/MPPBP]) were assessed. 1RMSQ (61.9 ± 13.3 kg) and MPPBP (207 ± 45 W) correlated significantly with 50 (1.84 ± 0.07 m∙s−1) and 100 m performance (1.68 ± 0.06 m∙s−1) (r ≥ 0.45) and ċLamax (0.35 ± 0.12 mmol·L−1·s−1) and body mass (60.1 ± 7.0 kg) with 50 and 100 m, respectively (r ≥ 0.44). Only LT1 (1.23 ± 0.04 m∙s−1) correlated significantly with 200 (1.52 ± 0.05 m∙s−1) and 400 m performance (1.43 ± 0.06 m∙s−1) (r ≥ 0.56). Multiple regression explained 33–35% and 61–86% of the variance in short- and middle-distance performance based on 1RMSQ and arm span and LT1, C, and fat percentage, respectively. Based on the analyses, mechanical determinants are more predictive of short- and metabolic determinants of middle-distance performance.
Ligand-receptor interactions combined with histopathology for improved prognostic modeling in HPV-negative head and neck squamous cell carcinoma
Authors: Feng Bohai , Zhao Di , Zhang Zheng , Jia Ru , Schuler Patrick J. , Hess Jochen 
Published: February 2025

Abstract
Head and neck squamous cell carcinoma (HNSC) is a prevalent malignancy, with HPV-negative tumors exhibiting aggressive behavior and poor prognosis. Understanding the intricate interactions within the tumor microenvironment (TME) is crucial for improving prognostic models and identifying therapeutic targets. Using BulkSignalR, we identified ligand-receptor interactions in HPV-negative TCGA-HNSC cohort (n = 395). A prognostic model incorporating 14 ligand-receptor pairs was developed using random forest survival analysis and LASSO-penalized Cox regression based on overall survival and progression-free interval of HPV-negative tumors from TCGA-HNSC. Multi-omics analysis revealed distinct molecular features between risk groups, including differences in extracellular matrix remodeling, angiogenesis, immune infiltration, and APOBEC enzyme activity. Deep learning-based tissue morphology analysis on HE-stained whole slide images further improved risk stratification, with region selection via Silicon enhancing accuracy. The integration of routine histopathology with deep learning and multi-omics data offers a clinically accessible tool for precise risk stratification, facilitating personalized treatment strategies in HPV-negative HNSC.
Identification of coevolving positions by ancestral reconstruction
Authors: Nelson Michael G. , Talavera David 
Published: February 2025

Abstract
Coevolution within proteins occurs when changes in one position affect the selective pressure in another position to preserve the protein structure or function. The identification of coevolving positions within proteins remains contentious, with most methods disregarding the phylogenetic information. Here, we present a time-efficient approach for detecting coevolving pairs, which is almost perfect in terms of precision and specificity. It is based on maximum parsimony-based ancestral reconstruction followed by the identification of pairs with a depletion on separate changes when compared to their number of concurrent changes. Our analysis of a previously characterised biological dataset shows that the coevolving pairs that we identified tend to be close in the protein sequence and structure, slightly less solvent exposed and have a higher mutation rate. We also show how the ancestral reconstruction can be used to detect favourable and unfavourable amino acid combinations. Altogether, we demonstrate how this approach is essential for identifying pairs of positions with weak covariation patterns.
The type of carbon source not the growth rate it supports can determine diauxie in Saccharomyces cerevisiae
Authors: Huo Yu , Danecka Weronika , Farquhar Iseabail , Mailliet Kim , Moses Tessa , Wallace Edward W. J. , Swain Peter S. 
Published: February 2025

Abstract
How cells choose between carbon sources is a classic example of cellular decision-making. Microbes often prioritise glucose, but there has been little investigation of whether other sugars are also preferred. Here we study budding yeast growing on mixtures of sugars with palatinose, a sucrose isomer that cells catabolise with the MAL regulon. We find that the decision-making involves more than carbon flux-sensing: yeast prioritise galactose over palatinose, but sucrose and fructose weakly if at all despite each allowing faster growth than palatinose. With genetic perturbations and transcriptomics, we show that the regulation is active with repression of the MAL genes via Gal4, the GAL regulon’s master regulator. We argue, using mathematical modelling, that cells enforce their preference for galactose through weakening the MAL regulon’s positive feedback. They do so through decreasing intracellular palatinose by repressing MAL11, the palatinose transporter, and expressing the isomaltases IMA1 and IMA5. Supporting these predictions, we show that deleting IMA1 abolishes diauxie. Our results demonstrate that budding yeast actively prioritises carbon sources other than glucose and that such priorities need not reflect differences in growth rates. They imply that carbon-sensing strategies even in model organisms are more complex than previously thought.
Metastable phase-separated droplet generation and long-time DNA enrichment by laser-induced Soret effect
Authors: Kobayashi Mika , Minagawa Yoshihiro , Noji Hiroyuki 
Published: February 2025

Abstract
Spatiotemporally controlled laser-induced phase separation (LIPS) offers unique research avenues and has potential for biological and biomedical applications. However, LIPS conditions often have drawbacks for practical use, which limit their applications. For instance, LIPS droplets are unstable and diminish after the laser is terminated. Here, we developed a novel LIPS method using laser-induced Soret effect with a simple setup to solve these problems. We generate liquid-liquid phase-separated (LLPS) droplets using LIPS in an aqueous two-phase system (ATPS) of dextran (DEX) and polyethylene glycol (PEG). When DEX-rich droplets were generated in the DEX/PEG mix on the phase boundary, the droplets showed unprecedently high longevity; the DEX droplets were retained over 48 h. This counterintuitive behaviour suggests that the droplet is in an unknown metastable state. By exploiting the capability of DEX-rich droplets to enrich nucleic acid polymers, we achieved stable DNA enrichment in LIPS DEX droplets with a high enrichment factor of 1400 ± 400. Further, we patterned DNA-carrying DEX-rich droplets into a designed structure to demonstrate the stability and spatiotemporal controllability of DEX-rich droplet formation. This is the first report for LIPS droplet generation in a DEX/PEG system, opening new avenues for biological and medical applications of LIPS.
Aspergillus fumigatus mitogenomes and their influence on azole-resistant and -susceptible populations
Authors: Delbaje Endrews , Pontes Laís , Rhodes Johanna , Steenwyk Jacob , Lu Ling , dos Reis Thaila F. , Rokas Antonis , Goldman Gustavo H. 
Published: February 2025

Abstract
The role of the fungal mitochondria goes far beyond energy metabolism. The genomes of 318 Aspergillus fumigatus clinical and environmental isolates from different geographic origins were analyzed aiming to study the mitochondrial sequences from populations sensitive and resistant to azoles. Our results show that A. fumigatus mitogenomic sequences are very conserved and only show variation in small intergenic regions and one intronic sequence in the cox3 gene. Furthermore, a genome-wide association analysis of accessory mitochondrial genes revealed potential mitochondria-based genotypes that may interact synergistically with the ergosterol biosynthesis pathway to confer the resistant phenotype. This includes a mutation in the AMID-like mitochondrial oxidoreductase (aifA, AFUA_3G01290) and the absence of the mitochondrial carrier protein (pet8, AFUA_8G01400). Deletion of these genes did not change the azole-susceptibility but increased the azole-persistence, suggesting mitochondrial genes could be involved in azole-persistence. Our work opens new hypotheses for the involvement of mitochondria in A. fumigatus azole-resistance.