bluuebunny/crossref_metadata_2025_split huggingface dataset:
Accelerating charge estimation in molecular dynamics simulations using physics-informed neural networks: corrosion applications
Authors: Venkatraman Aditya , Wilson Mark A. , Montes de Oca Zapiain David 
Published: February 2025

Abstract
Molecular Dynamics (MD) simulations are used to understand the effects of corrosion on metallic materials in salt brine. Reactive force fields in classical MD enable accurate modeling of bond formation and breakage in the aqueous medium and at the metal-electrolyte interface, while also facilitating dynamic partial charge equilibration. However, MD simulations are computationally intensive and unsuitable for modeling the long time scales characteristic of corrosive phenomena. To address this, we develop reduced-order machine learning models that provide accurate and efficient predictions of charge density in corrosive environments. Specifically, we use Long Short-Term Memory (LSTM) networks to forecast charge density evolution based on atomic environments represented by Smooth Overlap of Atomic Positions (SOAP) descriptors. A physics-informed loss function enforces charge neutrality and electronegativity equivalence. The atomic charges predicted by the deep learning model trained on this work were obtained two orders of magnitude faster than those from molecular dynamics (MD) simulations, with an error of less than 3% compared to the MD-obtained charges, even in extrapolative scenarios, while adhering to physical constraints. This demonstrates the excellent accuracy, computational efficiency, and validity of the developed model. Lastly, even though developed for corrosion, these protocols are formulated in a phenomenon-agnostic manner, allowing application to various variable-charge interatomic potentials and related fields.
Fine-scale population structure and widespread conservation of genetic effect sizes between human groups across traits
Authors: Hu Sile , Ferreira Lino A. F. , Shi Sinan , Hellenthal Garrett , Marchini Jonathan , Lawson Daniel J. , Myers Simon R. 
Published: February 2025

Abstract
Understanding genetic differences between populations is essential for avoiding confounding in genome-wide association studies and improving polygenic score (PGS) portability. We developed a statistical pipeline to infer fine-scale Ancestry Components and applied it to UK Biobank data. Ancestry Components identify population structure not captured by widely used principal components, improving stratification correction for geographically correlated traits. To estimate the similarity of genetic effect sizes between groups, we developed ANCHOR, which estimates changes in the predictive power of an existing PGS in distinct local ancestry segments. ANCHOR infers highly similar (estimated correlation 0.98 ± 0.07) effect sizes between UK Biobank participants of African and European ancestry for 47 of 53 quantitative phenotypes, suggesting that gene–environment and gene–gene interactions do not play major roles in poor cross-ancestry PGS transferability for these traits in the United Kingdom, and providing optimism that shared causal mutations operate similarly in different populations.
A barley pan-transcriptome reveals layers of genotype-dependent transcriptional complexity
Authors: Guo Wenbin , Schreiber Miriam , Marosi Vanda B. , Bagnaresi Paolo , Jørgensen Morten Egevang , Braune Katarzyna B. , Chalmers Ken , Chapman Brett , Dang Viet , Dockter Christoph , Fiebig Anne , Fincher Geoffrey B. , Fricano Agostino , Fuller John , Haaning Allison , Haberer Georg , Himmelbach Axel , Jayakodi Murukarthick , Jia Yong , Kamal Nadia , Langridge Peter , Li Chengdao , Lu Qiongxian , Lux Thomas , Mascher Martin , Mayer Klaus F. X. , McCallum Nicola , Milne Linda , Muehlbauer Gary J. , Nielsen Martin T. S. , Padmarasu Sudharsan , Pedas Pai Rosager , Pillen Klaus , Pozniak Curtis , Rasmussen Magnus W. , Sato Kazuhiro , Schmutzer Thomas , Scholz Uwe , Schüler Danuta , Šimková Hana , Skadhauge Birgitte , Stein Nils , Thomsen Nina W. , Voss Cynthia , Wang Penghao , Wonneberger Ronja , Zhang Xiao-Qi , Zhang Guoping , Cattivelli Luigi , Spannagl Manuel , Bayer Micha , Simpson Craig , Zhang Runxuan , Waugh Robbie 
Published: February 2025

Abstract
A pan-transcriptome describes the transcriptional and post-transcriptional consequences of genome diversity from multiple individuals within a species. We developed a barley pan-transcriptome using 20 inbred genotypes representing domesticated barley diversity by generating and analyzing short- and long-read RNA-sequencing datasets from multiple tissues. To overcome single reference bias in transcript quantification, we constructed genotype-specific reference transcript datasets (RTDs) and integrated these into a linear pan-genome framework to create a pan-RTD, allowing transcript categorization as core, shell or cloud. Focusing on the core (expressed in all genotypes), we observed significant transcript abundance variation among tissues and between genotypes driven partly by RNA processing, gene copy number, structural rearrangements and conservation of promotor motifs. Network analyses revealed conserved co-expression module::tissue correlations and frequent functional diversification. To complement the pan-transcriptome, we constructed a comprehensive cultivar (cv.) Morex gene-expression atlas and illustrate how these combined datasets can be used to guide biological inquiry.
Potato starch quality in relation to the treatments and long-term storage of tubers
Authors: Brążkiewicz Katarzyna , Pobereżny Jarosław , Wszelaczyńska Elżbieta , Bogucka Bożena 
Published: February 2025

Abstract
Starch is the most important component of potato tubers, the structure and composition of which play a key role in their utilization as well as processing and storage. Potato seed treatment may play the biggest role in ensuring proper plant growth and development. Isolated starch from tubers of different potato varieties for food processing was evaluated immediately after harvesting and after long-term storage with consideration of different products for potato seed treatment. The quality of starch was evaluated in terms of starch grain size, total starch content, pH, starch stability after freezing, gelatinization temperature as well as phosphorus and amylose content. The varieties differed significantly in starch content and quality. The Beo variety had the highest starch content in dry tuber weight (77.8%) and the best starch quality characteristics (the lowest starch stability after freezing − 18.0% and highest gelatinization temperature − 64.0 ℃ onset and 68.4 ℃ end). Simultaneous treatment of tubers with Supporter and Moncut 460 SC contributed to the highest starch content in tubers (77.6% d. m.) as well as increasing its stability after freezing (20.8%) and decreasing its gelatinization temperatures (61.7 ℃ onset and 66.0 ℃ end). This may be due to the increased proportion of large starch grains and higher amylose and phosphorus content. A slight decrease in starch quality traits was shown after long-term storage of tubers. Maintaining constant conditions during storage along with the applied treatments contributed to this. It is recommended to use products for potato seed treatment in the production technology of potato for consumption and for starch production.
Cost-effective adsorption of cationic dyes using ZnO nanorods supported by orange peel-derived carbon
Authors: Abdelrazek Eman J. E. , Gahlan Ahmed A. , Gouda Gamal A. , Ahmed Abdelaal S. A. 
Published: February 2025

Abstract
Here, porous carbon (PC) and ZnO nanorods@PC (ZnO-NR@PC) composite derived from orange peel (OP) have been synthesized via a simple carbonization process. The prepared materials have been characterized by XRD, FT-IR, TEM, and BET analysis. The adsorptive properties of the prepared PC and ZnO-NR@PC composite have been investigated toward methylene blue (MB) and crystal violet (CV) cationic dyes from their aqueous solutions. The adsorption studies concluded that the maximum adsorption efficiency was achieved after 90 min in the basic conditions (pH = 10). Langmuir, Freundlich, Dubinin–Radushkevich (D-R), and Temkin non-linear isotherm models were applied to fit the experimental data. The adsorption of MB and CV dyes by the OP is fitted with the Freundlich model, and the adsorption of both dyes by the PC and the ZnO-NR@PC composite fitted with the Langmuir model. The estimated maximum adsorption capacity estimated from the adsorption of MB and CV by the ZnO-NR@PC composite was 74.45 and 74.89 mg/g, respectively. The calculated adsorption free energy from D-R and Temkin models indicates the adsorption of MB, and CV dye molecules by the OP, PC, and ZnO-NR@PC composite may be physical. The kinetic studies revealed the adsorption of MB and CV dyes onto the OP, PC and ZnO-NR@PC composite fitted with the pseudo-second-order model. On the otherhand, the thermodynamic studies confirmed the adsorption of MB, and CV dyes onto ZnO-NR@PC composite is an endothermic and spontaneous process. Furthermore, the prepared materials displayed high adsorption stability with an overall removal efficiency of about 90% after five cycles. The mechanism of MB and CV dyes by the ZnO-NR@PC composite is proposed to be controlled by electrostatic bonding, π-π interactions, and ion exchange. The results indicated the potential ability of OP-derived porous carbons as adsorbents for cationic dyes from aqueous media.
Perovskite-driven solar C2 hydrocarbon synthesis from CO2
Authors: Andrei Virgil , Roh Inwhan , Lin Jia-An , Lee Joshua , Shan Yu , Lin Chung-Kuan , Shelton Steve , Reisner Erwin , Yang Peidong 
Published: February 2025

Abstract
Photoelectrochemistry (PEC) presents a direct pathway to solar fuel synthesis by integrating light absorption and catalysis into compact electrodes. Yet, PEC hydrocarbon production remains elusive due to high catalytic overpotentials and insufficient semiconductor photovoltage. Here we demonstrate ethane and ethylene synthesis by interfacing lead halide perovskite photoabsorbers with suitable copper nanoflower electrocatalysts. The resulting perovskite photocathodes attain a 9.8% Faradaic yield towards C2 hydrocarbon production at 0 V against the reversible hydrogen electrode. The catalyst and perovskite geometric surface areas strongly influence C2 photocathode selectivity, which indicates a role of local current density in product distribution. The thermodynamic limitations of water oxidation are overcome by coupling the photocathodes to Si nanowire photoanodes for glycerol oxidation. These unassisted perovskite–silicon PEC devices attain partial C2 hydrocarbon photocurrent densities of 155 µA cm−2, 200-fold higher than conventional perovskite–BiVO4 artificial leaves for water and CO2 splitting. These insights establish perovskite semiconductors as a versatile platform towards PEC multicarbon synthesis.
Dynamic land-plant carbon sources in marine sediments inferred from ancient DNA
Authors: Herzschuh Ulrike , Weiß Josefine Friederike , Stoof-Leichsenring Kathleen R. , Harms Lars , Nürnberg Dirk , Müller Juliane 
Published: February 2025

Abstract
Terrigenous organic matter in marine sediments is considered a significant long-term carbon sink, yet our knowledge regarding its source taxa is severely limited. Here, we leverage land-plant ancient DNA from six globally distributed marine sediment cores covering the Last Glacial–Holocene transition as a proxy for the share, burial rate, preservation, and composition of terrigenous organic matter. We show that the spatial and temporal plant composition as revealed by sedimentary ancient DNA records reflects mainly the vegetation dynamics of nearby continents as revealed by comparison with pollen from land archives. However, we also find indications of a global north-to-south translocation of sedimentary ancient DNA. We also find that plant sedimentary ancient DNA has a higher burial rate in samples from the Late Glacial, which is characterized by high runoff and mineral load. This study provides an approach to understanding the global linkages between the terrestrial and marine carbon cycle, highlighting the need for further research to quantify the processes of DNA preservation and dispersal in marine sediments.
Earthquake-based multi-hazard resilience assessment: a case study of Istanbul, Turkey (neighborhood level)
Authors: Ghaffarian Saman , Shafapourtehrany Mahyat , Lagap Umut , Batur Maryna , Özener Haluk , Kılcı Rıza Evren , Karaman Himmet 
Published: February 2025

Abstract
We developed a model integrating 28 criteria spanning social, economic, community, environmental, and physical dimensions to evaluate earthquake resilience of Istanbul, a city with a population of 16 million and significant seismic risk, at both district and subdistrict/neighborhood levels. The resilience assessment uses the Bayesian Best-Worst Method, a multi-criteria decision-making framework that combines expert knowledge and statistical assessments. The results reveal that Istanbul’s overall Resilience Score (RS) is 0.48, on a 0-1 scale, suggesting a moderate capacity to endure and recover from seismic events. Catalca, Adalar, and Arnavutkoy rank among the most resilient districts, whereas Esenler and Gungoren exhibit lower resilience. On a subdistrict level, Suleymaniye (Fatih) has the highest RS at 0.59, while Yavuz Sultan Selim (Fatih) ranks the lowest with 0.22. These findings provide actionable and practical data-driven insights for policymakers and urban planners, underscoring the need for targeted interventions to improve resilience in high-risk areas in Istanbul.
An AI-assisted morphoproteomic approach is a supportive tool in esophagitis-related precision medicine
Authors: Mattern Sven , Hollfoth Vanessa , Bag Eyyub , Ali Arslan , Riemenschneider Philip , Jarboui Mohamed A , Boldt Karsten , Sulyok Mihaly , Dickemann Anabel , Luibrand Julia , Fusco Stefano , Franz-Wachtel Mirita , Singer Kerstin , Goeppert Benjamin , Schilling Oliver , Malek Nisar , Fend Falko , Macek Boris , Ueffing Marius , Singer Stephan 
Published: February 2025

Abstract
Esophagitis is a frequent, but at the molecular level poorly characterized condition with diverse underlying etiologies and treatments. Correct diagnosis can be challenging due to partially overlapping histological features. By proteomic profiling of routine diagnostic FFPE biopsy specimens (n = 55) representing controls, Reflux- (GERD), Eosinophilic-(EoE), Crohn’s-(CD), Herpes simplex (HSV) and Candida (CA)-esophagitis by LC-MS/MS (DIA), we identified distinct signatures and functional networks (e.g. mitochondrial translation (EoE), immunoproteasome, complement and coagulations system (CD), ribosomal biogenesis (GERD)), and pathogen-specific proteins for HSV and CA. Moreover, combining these signatures with histological parameters in a machine learning model achieved high diagnostic accuracy (100% training set, 93.8% test set), and supported diagnostic decisions in borderline/challenging cases. Applied to a young patient representing a use case, the external GERD diagnosis could be revised to CD and ICAM1 was identified as highly abundant therapeutic target. This resulted in CyclosporinA as a personalized treatment recommendation by the local multidisciplinary molecular inflammation board. Our integrated AI-assisted morphoproteomic approach allows deeper insights in disease-specific molecular alterations and represents a promising tool in esophagitis-related precision medicine.
Calcium-mediated regulation of mitophagy: implications in neurodegenerative diseases
Authors: Borbolis Fivos , Ploumi Christina , Palikaras Konstantinos 
Published: February 2025

Abstract
Calcium signaling plays a pivotal role in diverse cellular processes through precise spatiotemporal regulation and interaction with effector proteins across distinct subcellular compartments. Mitochondria, in particular, act as central hubs for calcium buffering, orchestrating energy production, redox balance and apoptotic signaling, among others. While controlled mitochondrial calcium uptake supports ATP synthesis and metabolic regulation, excessive accumulation can trigger oxidative stress, mitochondrial membrane permeabilization, and cell death. Emerging findings underscore the intricate interplay between calcium homeostasis and mitophagy, a selective type of autophagy for mitochondria elimination. Although the literature is still emerging, this review delves into the bidirectional relationship between calcium signaling and mitophagy pathways, providing compelling mechanistic insights. Furthermore, we discuss how disruptions in calcium homeostasis impair mitophagy, contributing to mitochondrial dysfunction and the pathogenesis of common neurodegenerative diseases.
Addressing myocardial infarction in South-Asian populations: risk factors and machine learning approaches
Authors: Rejeleene Rick , Chidambaram Vignesh , Chatrathi Meena , Kumar Amudha , Lu Emily , Pohlkamp Ryan , Murthi Mukunthan , Gautam Nitesh , Al’Aref Subhi , Xu Xiaowei , Mehta Jawahar L. 
Published: February 2025

Abstract
Cardiovascular diseases, especially myocardial infarction (MI), are an important and up-trending public health challenge in the South Asian population. With urbanization and economic development, there has been a rise in obesity, dyslipidemia, diabetes mellitus, and hypertension in these regions, which, combined with genetic predisposition, create a unique cardiovascular risk profile among South Asians. Traditional risk assessment tools often underestimate the cardiovascular risk in South Asians due to a lack of phenotypic representation in their development. In this review, we explore the risk factors for MI in South Asians and highlight the potential role of machine learning (ML) and deep learning (DL) in enhancing diagnostic and predictive accuracy. These ML algorithms, including convolutional neural networks (CNNs) and transformer-based models, show potential in analyzing complex information from clinical characteristics, electrocardiograms (ECG), and cardiac biomarkers while integrating multimodal data. We also explore the challenges in accessing high-quality datasets and enabling applicability in clinical settings. We believe that future research should focus on developing comprehensive cardiovascular risk scores that incorporate South Asian-specific risk factors and leverage advanced ML models to enhance risk prediction, diagnosis, and management.
Predicting drug combination response surfaces
Authors: Huusari Riikka , Wang Tianduanyi , Szedmak Sandor , Aittokallio Tero , Rousu Juho 
Published: February 2025

Abstract
Prediction of drug combination responses is a research question of growing importance for cancer and other complex diseases. Current machine learning approaches generally consider predicting either drug combination synergy summaries or single combination dose-response values, which fail to appropriately model the continuous nature of the underlying dose-response combination surface and can lead to inconsistencies when a synergy score or a dose-response matrix is reconstructed from separate predictions. We propose a novel prediction method, comboKR, that directly predicts the continuous drug combination response surface for a drug combination. The method is based on a powerful input–output kernel regression technique and functional modelling of the response surface. ComboKR belongs to the family of functional output regression methods, where the prediction target is a function, in our case, a non-linear parametric surface. Our method thus avoids predicting discretized forms of the target as scalars, vectors or matrices, and therefore provides better interpolation and extrapolation along the surfaces. As an important part of our approach, we develop a novel normalisation between response surfaces that standardises the heterogeneous experimental designs used to measure the dose-responses, and thus allows training the method with data measured in different laboratories. Our experiments on two predictive scenarios and using two combination datasets highlight the suitability of the proposed approach especially in the traditionally challenging setting of predicting combination responses for new drugs not available in the training data.
Heterogeneous biocatalysis by magnetic nanoparticle immobilized biomass-degrading enzymes derived from microbial cultures
Authors: Gupta Mani , Bandyopadhyay Arghya , Sinha Sushant Kumar , Suksham  , Samanta Abhisek , Mondal Surajit , Bhattacharyya Sayan , Datta Supratim 
Published: February 2025

The NTA–Ni@Fe3O4 NPs can capture enzymes from cell lysates and microbial culture media. We developed a model system to show the efficacy of immobilizing and recycling biomass-degrading enzymes known as cellulases.
Mütter mit Migrationshintergrund kennen Unterstützungsangebote vor
und nach der Geburt signifikant weniger als Mütter ohne Migrationshintergrund:
querschnittliche Erhebung in zwei Geburtskliniken
Authors: Selbertinger Benedikt , Rothfuß David , Hatzipanagiotou Maria , Köninger Angela , Apfelbacher Christian , Kabesch Michael , Brandstetter Susanne 
Published: February 2025

ZusammenfassungAbhängig vom Migrationshintergrund galt es, Kenntnis von
Unterstützungsangeboten für die Zeit der Schwangerschaft und der ersten
Lebensjahre des Kindes zu beschreiben und zu analysieren.Fragebögen wurden in 26 verschiedenen Sprachen angeboten. Mittels
Querschnittdesign wurden 641 Wöchnerinnen zur Kenntnis von 11 in der Region
verfügbaren Unterstützungsangeboten, der Informationsquelle bekannter
Angebote und zu weiteren potenziellen Informationsvermittlern befragt.
Mittels Median wurde gute und schlechte Kenntnis der Angebote
unterschieden.Die Muttersprache von 30% der Befragten (n=194) war nicht Deutsch; diese
wurden für die Studie als „Mütter mit Migrationshintergrund“ definiert. Von
den Müttern mit Migrationshintergrund kannten 83% (n=161) die Hebamme als
Unterstützungsangebot. Die Bekanntheitsgrade der anderen
Unterstützungsangebote lagen mit<40% weit unter denjenigen der Mütter
ohne Migrationshintergrund. Frauen mit MH kannten die Unterstützungsangebote
signifikant seltener (OR 0,16, 95% KI 0,11 bis 0,23), auch wenn für weitere
soziodemographische Variablen kontrolliert wurde. Außerdem gaben sie
signifikant seltener an, ihre Informationen über die Angebote durch
Frauenärzt*innen, Hebammen, Verwandte/ Bekannte, oder Eigenrecherche
erhalten zu haben.Kenntnisse zu Unterstützungsangeboten vor und nach der Geburt müssen vor
allem für Mütter mit Migrationshintergrund besser zugänglich werden.
Hebammen könnten dabei eine entscheidende Rolle spielen.
Die Krankenhausreform
Authors: Schaffert Reinhard 
Published: February 2025

ZusammenfassungViele Auswirkungen der neuen Gesetze bleiben für Kliniken unklar, bis die neue Regierung ihre Arbeit aufgenommen und noch fehlende Rechtsverordnungen nachgeholt hat. Trotzdem lohnt sich ein Blick in die Zukunft.
Künstliche Intelligenz in der Kinderpneumologie – Chancen und
unbeantwortete Fragen
Authors: Dramburg Stephanie 
Published: February 2025

ZusammenfassungKünstliche Intelligenz (KI) findet bereits Anwendung in den meisten medizinischen
Disziplinen, so auch in der pädiatrischen Pneumologie. Diese Übersichtsarbeit
beschreibt aktuelle Entwicklungen KI-gestützter Technologien und erörtert deren
Potenzial für die Diagnostik und Behandlung von Lungenerkrankungen im Kindes-
und Jugendalter. Das Spektrum reicht von Modellen zur Analyse von
Atemgeräuschen, über die automatisierte Auswertung medizinischer Bildgebung bis
hin zu Systemen zur Unterstützung klinischer Entscheidungen. Hierbei werden
insbesondere auch Herausforderungen bei der Anpassung von KI für pädiatrische
Bevölkerungsgruppen beschrieben. Schließlich werden offene Fragen,
beispielsweise zur Implementierung KI-basierter Software in den klinischen
Alltag erörtert.