bluuebunny/crossref_metadata_2025_split huggingface dataset:
Running in mice increases the expression of brain hemoglobin-related genes interacting with the GH/IGF-1 system
Authors: Walser Marion , Karlsson Lars , Motalleb Reza , Isgaard Jörgen , Kuhn H. Georg , Svensson Johan , Åberg N. David 
Published: October 2024

AbstractThe beneficial effects of exercise are partly mediated via local or systemic functions of the insulin-like growth factor-1 (IGF-1) system. As IGF-1 increases local brain hemoglobin beta (Hbb) transcripts, we hypothesized that exercise could have similar effects. Mice were single-housed with free access to running wheels for seven days. After sacrifice and saline perfusion, the expression of 13 genes was quantified using real-time quantitative polymerase chain reaction (RT-qPCR) in three brain regions: the prefrontal cortex, motor cortex, and hippocampus. In addition, plasma insulin, glucose, homeostatic model assessment of IR (HOMA-IR), C-peptide, and IGF-1 were investigated. We show that hemoglobin-related transcripts (Hbb and 5’-aminolevulinate synthase 2 [Alas2]) increased 46–63% in the running group, while IGF-1-related genes [Igf1 / growth hormone receptor (Ghr)] decreased slightly (7%). There were also moderate to large correlations between Hbb- and IGF-1-related genes in the running group but not in the sedentary group. HOMA-IR, plasma glucose, and insulin changed marginally and non-significantly, but there was a trend toward an increase in plasma-IGF-1 in the running group. In conclusion, seven days of running increased Hbb-related transcripts in three brain regions. Hbb-related transcripts correlated with components of the brain IGF-1 system only in the running group.
Mapping the energy-momentum dispersion of hBN excitons and hybrid plasmons in hBN-WSe2 heterostructures
Authors: Nerl Hannah C. , Guerrero-Felipe Juan Pablo , Valencia Ana M. , Elyas Khairi Fahad , Höflich Katja , Koch Christoph T. , Cocchi Caterina 
Published: October 2024

AbstractHeterostructures obtained by combining two-dimensional (2D) sheets are widely investigated as a platform for designing new materials with customised characteristics. Transition metal dichalcogenides (TMDCs) are often combined with hexagonal boron nitride (hBN) to enhance their excitonic resonances. However, little is known about how stacking affects excitons and plasmons in TMDCs or their mutual interactions. Here, we combine momentum-resolved electron energy-loss spectroscopy with first-principles calculations to study the energy-momentum dispersion of plasmons in multi-layer WSe2-hBN heterostructures as well as in their isolated components. The dispersion of the high-momentum excitons of hBN, alone and in combination with WSe2, is mapped across the entire Brillouin zone. Signatures of hybridisation in the plasmon resonances and some of the excitons suggest that the contribution of hBN cannot be neglected when interpreting the response of such a heterostructure. The consequences of using hBN as an encapsulant for TMDCs are also discussed.
Water-surface photovoltaic systems have affected water physical and chemical properties and biodiversity
Authors: Yang Sen , Zhang Youzheng , Tian Defeng , Liu Zekang , Ma Zhijun 
Published: October 2024

AbstractThe implementation of water-surface photovoltaic systems as a source of renewable power has expanded rapidly worldwide in recent decades. Water-surface photovoltaic avoids negative impacts on terrestrial ecosystems, while the impacts on aquatic physical and chemical properties and biodiversity are unclear. To understand the ecological and environmental impacts of water-surface photovoltaic systems, here we conducted a field survey on water physical and chemical properties, plankton and bird communities of 26 water-surface photovoltaic systems in the Yangtze River basin in China during the winter and summer of 2022. We found that water-surface photovoltaic systems decreased water temperature, dissolved oxygen saturation and uncovered area of the water surface, which caused a reduction in plankton species and individual density, altering the community composition. Water-surface photovoltaic systems also caused an overall decrease in bird diversity and changed bird community compositions. These findings suggested that water-surface photovoltaic systems have impacts on the water environment and ecology. Since water-surface photovoltaic systems will continue to expand in the future, our results emphasize that rational planning is critical for the sustainable development of water-surface photovoltaic systems and the protection of the aquatic environment and biodiversity.
Thermal optimization of PCM‐based heat sink using fins: A combination of CFD, genetic algorithms, and neural networks
Authors: Bahrami Hamid‐Reza , Saberi Mehdi 
Published: October 2024

AbstractSustainable development requires focusing on renewable energy, such as solar power, due to the limited availability of fossil fuels. Solar energy, available only during daylight, can be stored in thermal batteries using phase change materials (PCM). However, PCM has low thermal conductivity, slowing its melting process. Fins have been added to enhance heat diffusion, but their optimal design requires further study. This research examines an annular chamber with fixed inner and outer wall temperatures and explores fin configurations. Two series of evenly spaced fins (four and five) were analyzed, with melting times calculated using CFD simulations. Larger fins reduce melting time but limit PCM storage. Effectiveness, defined as the PCM space over melting time, was used to evaluate performance. Higher boundary temperatures decreased melting time but did not always increase effectiveness. The study also used artificial neural networks (ANN) combined with a genetic algorithm to predict optimal conditions. The configuration with five fins, a 90°C boundary temperature, a fin length of 45 mm, and a thickness of 5.4 mm achieved the highest effectiveness of 4.7, showing that smaller fins at lower temperatures are more efficient.
The rotational disruption of porous dust aggregates from ab initio kinematic calculations
Authors: Reissl Stefan , Nguyen Philipp , Jordan Lucas M. , Klessen Ralf S. 
Published: October 2024

Context. The size of dust grains in the interstellar medium follows a distribution where most of the dust mass is made up of smaller grains. However, the redistribution from larger grains towards smaller sizes, especially by means of rotational disruption, is still poorly understood.
Aims. We aim to study the dynamics of porous grain aggregates undergoing an accelerated rotation, namely, a spin-up process that rapidly increases the angular velocity of the aggregate. In particular, we aim to determine the deformation of the grains and the maximal angular velocity up to the rotational disruption event by caused by centrifugal forces.
Methods. We precalculated the porous grain aggregate by means of ballistic aggregation analogous to the interstellar dust as input for subsequent numerical simulations. We performed three-dimensional (3D) N-body simulations, mimicking the radiative torque spin-up process up to the point where the grain aggregates become rotationally disrupted.
Results. Our simulations results are in agreement with theoretical models predicting a characteristic angular velocity, ωdisr, on the order of 108–109 rad s−1, where grains become rotationally disrupted. In contrast to theoretical predictions, we show that for large porous grain aggregates (⪆300 nm), the ωdisr values do not strictly decline. Instead, they reach a lower asymptotic value. Hence, such grains can withstand an accelerated rotation more efficiently up to a factor of 10 because the displacement of mass by centrifugal forces and the subsequent mechanical deformation supports the buildup of new connections within the aggregate. Furthermore, we report that the rapid rotation of grains deforms an ensemble with initially 50:50 prolate and oblate shapes, respectively, preferentially into oblate shapes. Finally, we present a best-fit formula to predict the average rotational disruption of an ensemble of porous dust aggregates dependent on the internal grain structure, total number of monomers, and applied material properties.
Schuleingangsuntersuchungen in der ehemaligen Deutschen
Demokratischen Republik (DDR) von 1949 bis 1989 – Eine orientierende Übersicht
zur formalen und inhaltlichen Durchführung
Authors: Knüppel Clara Maria , Stich Heribert 
Published: October 2024

Zusammenfassung
Ziel der Studie Zusammenfassende Darstellung der formalen und inhaltlichen
Einschulungspraxis von Schuleingangsuntersuchungen (SEU) in der Deutschen
Demokratischen Republik (DDR).
Methoden Durch eine systematische Literatursichtung auf konventioneller
und internetbasierter Grundlage wurden nach ausgewählten Suchbegriffen
Fachpublikationen und themenbezogene Originaldokumente zielgerichtet verfügbar
gemacht, um wesentliche Merkmale von SEU in der DDR zu erfassen und in einem
Gesamtkontext darzustellen.
Ergebnisse In der DDR erfolgte im Alter von 6 Jahren die regelrechte
Einschulung, wobei deren Durchführungspraxis während der 1950er und 1960er Jahre
formal und inhaltlich uneinheitlich ausgestaltet war. Auf der Grundlage
verpflichtender Reihenuntersuchungen hatte damals die medizinische
Reihenuntersuchung für die Altersgruppe der 5- bis 6-jährigen Kinder formale
Gültigkeit als SEU. Fallbezogen führten die zuständigen Kinderärzte ergänzende,
nicht standardisierte Entwicklungstests durch. Unter anderem durch die
zunehmende Versorgungsdichte mit Kindergärten in der DDR wurde dem dortigen
Fachpersonal ab den 1970er Jahren eine stärkere Einbindung bei der Beurteilung
beschulungsrelevanter Kompetenzen bei Vorschulkindern zugedacht. Daneben gewann
die Reihenuntersuchung für 4- bis 5-jährige Kinder für entwicklungsdiagnostische
Belange zusätzlich an Relevanz. Während anfangs noch diverse Testverfahren
Anwendung fanden, entwickelte sich ab den 1970er Jahren ein standardisiertes
Instrumenteninventar zur Einschätzung der Lernfähigkeit, Denkentwicklung und
Sprachentwicklung sukzessiv heraus. Durch beide zeitlich gestaffelten SEU
konnten im Bedarfsfall frühzeitige Förderungsmaßnahmen bei
entwicklungsverzögerten Kindern ergriffen werden, sodass diesen betroffenen
Kindern adäquate Entwicklungs- und Bildungschancen eröffnet werden konnten. Im
Gefolge dieser Konzeption fand auch eine sich intensivierende, teilweise
informelle Kooperation zwischen dem Fachpersonal in den Kindergärten, den
Kinderärzten, den Eltern und den Schulleitungen statt.
Schlussfolgerung Insgesamt kann festgehalten werden, dass die SEU in der
DDR ab den 1970er Jahren auf ein fachlich durchdachtes Gesamtkonzept basierten.
Zur damaligen Zeit war diese standardisierte, gestaffelte und multidisziplinäre
Vorgehensweise in der DDR der Einschulungspraxis in Westdeutschland methodisch
überlegen.
Causal relationships between diseases mined from the literature improve the use of polygenic risk scores
Authors: Toonsi Sumyyah , Gauran Iris Ivy , Ombao Hernando , Schofield Paul N , Hoehndorf Robert 
Published: October 2024

Abstract

Motivation
Identifying causal relations between diseases allows for the study of shared pathways, biological mechanisms, and inter-disease risks. Such causal relations can facilitate the identification of potential disease precursors and candidates for drug re-purposing. However, computational methods often lack access to these causal relations. Few approaches have been developed to automatically extract causal relationships between diseases from unstructured text, but they are often only focused on a small number of diseases, lack validation of the extracted causal relations, or do not make their data available.

Results
We automatically mined statements asserting a causal relation between diseases from the scientific literature by leveraging lexical patterns. Following automated mining of causal relations, we mapped the diseases to the International Classification of Diseases (ICD) identifiers to allow the direct application to clinical data. We provide quantitative and qualitative measures to evaluate the mined causal relations and compare to UK Biobank diagnosis data as a completely independent data source. The validated causal associations were used to create a directed acyclic graph that can be used by causal inference frameworks. We demonstrate the utility of our causal network by performing causal inference using the do-calculus, using relations within the graph to construct and improve polygenic risk scores, and disentangle the pleiotropic effects of variants.

Availability and implementation
The data are available through https://github.com/bio-ontology-research-group/causal-relations-between-diseases.
Improving ncRNA family prediction using multi-modal contrastive learning of sequence and structure
Authors: Xu Ruiting , Li Dan , Yang Wen , Wang Guohua , Li Yang 
Published: October 2024

Abstract

Motivation
Recent advancements in high-throughput sequencing technology have significantly increased the focus on non-coding RNA (ncRNA) research within the life sciences. Despite this, the functions of many ncRNAs remain poorly understood. Research suggests that ncRNAs within the same family typically share similar functions, underlining the importance of understanding their roles. There are two primary methods for predicting ncRNA families: biological and computational. Traditional biological methods are not suitable for large-scale data prediction due to the significant human and resource requirements. Concurrently, most existing computational methods either rely solely on ncRNA sequence data or are exclusively based on the secondary structure of ncRNA molecules. These methods fail to fully utilize the rich multimodal information available from ncRNAs, thereby preventing them from learning more comprehensive and in-depth feature representations.

Results
To tackle these problems, we proposed MM-ncRNAFP, a multi-modal contrastive learning framework for ncRNA family prediction. We first used a pre-trained language model to encode the primary sequences of a large mammalian ncRNA dataset. Then, we adopted a contrastive learning framework with an attention mechanism to fuse the secondary structure information obtained by graph neural networks. The MM-ncRNAFP method can effectively fuse multi-modal information. Experimental comparisons with several competitive baselines demonstrated that MM-ncRNAFP can achieve more comprehensive representations of ncRNA features by integrating both sequence and structural information. This integration significantly enhances the performance of ncRNA family prediction. Ablation experiments and qualitative analyses were performed to verify the effectiveness of each component in our model. Moreover, since our model is pre-trained on a large amount of ncRNA data, it has the potential to bring significant improvements to other ncRNA-related tasks.

Availability and implementation
MM-ncRNAFP and the datasets are available at https://github.com/xuruiting2/MM-ncRNAFP.
Detection and quantification of introgression using Bayesian inference based on conjugate priors
Authors: Pfeifer Bastian , Kapan Durrell D , Herzog Sereina A 
Published: October 2024

Abstract

Summary
Introgression (the flow of genes between species) is a major force structuring the evolution of genomes, potentially providing raw material for adaptation. Here, we present a versatile Bayesian model selection approach for detecting and quantifying introgression, df-BF, that builds upon the recently published distance-based df statistic. Unlike df, df-BF accounts for the number of variant sites within a genomic region. The underlying model parameter of our df-BF method, here denoted as dfθ, accurately quantifies introgression, and the corresponding Bayes Factors (df-BF) enables weighing the strength of evidence for introgression. To ensure fast computation, we use conjugate priors with no need for computationally demanding MCMC iterations. We compare our method with other approaches including df, fd, Dp, and Patterson’s D using a wide range of coalescent simulations. Furthermore, we showcase the applicability of df-BF and dfθ using whole-genome mosquito data. Finally, we integrate the new method into the powerful genomics R-package PopGenome.

Availability and implementation
The presented methods are implemented within the R-package PopGenome (https://github.com/pievos101/PopGenome) and the simulation as the application results can be reproduced from the source code available from a dedicated GitHub repository (https://github.com/pievos101/Introgression-Simulation).
A conceptual classification scheme of invasion science
Authors: Musseau Camille L , Bernard-Verdier Maud , Heger Tina , Skopeteas Leonidas H , Strasiewsky David , Mietchen Daniel , Jeschke Jonathan M 
Published: October 2024

Abstract
In the era of big data and global biodiversity decline, there is a pressing need to transform data and information into findable and actionable knowledge. We propose a conceptual classification scheme for invasion science that goes beyond hypothesis networks and allows to organize publications and data sets, guide research directions, and identify knowledge gaps. Combining expert knowledge with literature analysis, we identified five major research themes in this field: introduction pathways, invasion success and invasibility, impacts of invasion, managing biological invasions, and meta-invasion science. We divided these themes into 10 broader research questions and linked them to 39 major hypotheses forming the theoretical foundation of invasion science. As artificial intelligence advances, such classification schemes will become important references for organizing scientific information. Our approach can be extended to other research fields, fostering cross-disciplinary connections to leverage the scientific knowledge needed to address Anthropocene challenges.
MBNL deficiency in motor neurons disrupts neuromuscular junction maintenance and gait coordination
Authors: Frison-Roche Charles , Demier Célia Martin , Cottin Steve , Lainé Jeanne , Arandel Ludovic , Halliez Marius , Lemaitre Mégane , Lornage Xavière , Strochlic Laure , Swanson Maurice S , Martinat Cécile , Messéant Julien , Furling Denis , Rau Frédérique 
Published: October 2024

Abstract
Muscleblind-like proteins (MBNLs) are a family of RNA-binding proteins that play essential roles in the regulation of RNA metabolism. Beyond their canonical role in RNA regulation, MBNL proteins have emerged as key players in the pathogenesis of myotonic dystrophy type 1. In myotonic dystrophy type 1, sequestration of MBNL proteins by expansion of the CUG repeat RNA leads to functional depletion of MBNL, resulting in deregulated alternative splicing and aberrant RNA processing, which underlie the clinical features of the disease.
Although attention on MBNL proteins has focused on their functions in skeletal muscle, new evidence suggests that their importance extends to motor neurons (MNs), pivotal cellular components in the control of motor skills and movement. To address this question, we generated conditional double-knockout (dKO) mice, in which Mbnl1 and Mbnl2 were specifically deleted in motor neurons (MN-dKO). Adult MN-dKO mice develop gait coordination deficits associated with structural and ultrastructural defects in the neuromuscular junction, indicating that MBNL activity in MNs is crucial for the maintenance of the neuromuscular junction. In addition, transcriptome analysis performed on the spinal cord of MN-dKO mice identified mis-splicing events in genes associated with synaptic transmission and neuromuscular junction homeostasis.
In summary, our results highlight the complex roles and regulatory mechanisms of MBNL proteins in MNs for muscle function and locomotion. This work provides valuable insights into fundamental aspects of RNA biology and offers promising avenues for therapeutic intervention in myotonic dystrophy type 1 and in a range of diseases associated with RNA dysregulation.
Environmental sustainability in dermatological surgery. Part 1: reducing carbon intensity
Authors: Ali Fatima ,   BSDS Sustainability Subgroup Collaborative, Ali Fatima , Abbott Rachel , Wernham Aaron , Nikookam Yasmin , Hunt William , Holloran Sophie , Chaolin Catriona , Ang Eshen , Charalambides Maria , Lowe Ashima , Brindley Luke , Bower Christopher , Varma Sandeep , Lam Minh , Veitch David , Recica Hilmi , Woo Wen Ai , Tso Simon , Doyle Claire , Wernham Aaron , Abbott Rachel 
Published: October 2024

Abstract
This two-part review addresses the pressing need for environmental sustainability in dermatological surgery, driven by the National Health Service’s commitment to net-zero emissions. Part 1 focuses on strategies to reduce the carbon intensity of dermatological procedures by adopting low-carbon alternatives and optimizing operational resource usage. Key strategies for a system-wide reduction in environmental impact include leveraging local suppliers to reduce transport emissions, streamlining care models, promoting efficient waste management and using mindful prescribing practices. Another aspect is integrating sustainability into dermatological education while minimizing the carbon footprint of surgical education. Additionally, the review provides a comprehensive overview of optimizing resource use in dermatological surgery, focusing on efficient management of consumables, equipment and energy. This includes recycling, waste segregation, transitioning to reusable personal protective equipment and surgical instruments, and applying energy-saving and sustainable water use practices. By implementing these strategies, dermatological surgery can significantly reduce its environmental impact while upholding high standards of patient care.
Image classification with deconvolution operation and augmentation
Authors: Kumar Sarkar Nayan , Marjit Singh Moirangthem , Nandi Utpal 
Published: October 2024

Abstract
Several image classification approaches have been evolved over the years utilizing convolutional neural network (CNN). In convolution operation of CNN, the shifting of kernels to overlapping regions of the image learns redundant data as the images are strongly correlated in reality. The redundant data make the neural network training a challenging task. Again, Deep Learning methods evaluated on small dataset yields degraded performance. To deal with these issues, a proposal is made in this paper that uses deconvolution operation to minimize correlations from images and data augmentation technique to increase the size of datasets. Plant Village, Tomato, and Covid-19 datasets were used for evaluating the performance of the proposed method. 70% of the datasets were used for training, 10% for validation, and 20% for testing purposes. The CIFAR10, MNIST, and Mini-ImageNet datasets were also considered for performance evaluation. The proposed method performed better than other existing methods in terms of classification accuracy.