bluuebunny/crossref_metadata_2025_split huggingface dataset:
Predicting synthetic mRNA stability using massively parallel kinetic measurements, biophysical modeling, and machine learning
Authors: Cetnar Daniel P. , Hossain Ayaan , Vezeau Grace E. , Salis Howard M. 
Published: November 2024

AbstractmRNA degradation is a central process that affects all gene expression levels, though it remains challenging to predict the stability of a mRNA from its sequence, due to the many coupled interactions that control degradation rate. Here, we carried out massively parallel kinetic decay measurements on over 50,000 bacterial mRNAs, using a learn-by-design approach to develop and validate a predictive sequence-to-function model of mRNA stability. mRNAs were designed to systematically vary translation rates, secondary structures, sequence compositions, G-quadruplexes, i-motifs, and RppH activity, resulting in mRNA half-lives from about 20 seconds to 20 minutes. We combined biophysical models and machine learning to develop steady-state and kinetic decay models of mRNA stability with high accuracy and generalizability, utilizing transcription rate models to identify mRNA isoforms and translation rate models to calculate ribosome protection. Overall, the developed model quantifies the key interactions that collectively control mRNA stability in bacterial operons and predicts how changing mRNA sequence alters mRNA stability, which is important when studying and engineering bacterial genetic systems.
Chemokine receptor CXCR7 antagonism ameliorates cardiac and renal fibrosis induced by mineralocorticoid excess
Authors: Wang Bing H. , Robert Remy , Marques Francine Z. , Rajapakse Niwanthi , Kiriazis Helen , Mackay Charles R. , Kaye David M. 
Published: November 2024

AbstractCardiorenal fibrosis is a common feature of chronic cardiovascular disease and recent data suggests that cytokines and chemokines may also drive fibrosis. Here we tested the hypothesis that CXCR7, a highly conserved chemokine receptor, contributes to cardiac and renal fibrosis. We generated an anti-mouse CXCR7-specific monoclonal antibody (CXCR7 mAb) and tested its anti-fibrotic actions in cardiorenal fibrosis induced using the deoxycorticosterone acetate/uni-nephrectomy (DOCA-UNX) model. CXCR7 mAb treatment (10 mg/kg, twice weekly for 6 weeks) significantly attenuated the development of cardiac and renal fibrosis, and reduced fibrotic and inflammatory gene expression levels, in the absence of an effect on blood pressure. Immunohistochemical analysis demonstrated an increase in the vascular expression of CXCR7 in DOCA-UNX-treated mice. This study demonstrated that a CXCR7 mediated pathway plays a significant role in cardiac and renal fibrosis induced by DOCA-UNX treatment. Accordingly, antagonism of CXCR7 may provide a therapeutic opportunity to mitigate against fibrosis in the setting of mineralocorticoid excess.
Extracellular glutamate is not modulated by cannabinoid receptor activity
Authors: Chiu Delia N. , Carter Brett C. 
Published: November 2024

AbstractCannabinoid receptor activation has been proposed to trigger glutamate release from astrocytes located in cortical layer 2/3. Here, we measure the basal concentration of extracellular glutamate in layer 2/3 of mouse somatosensory cortex and find it to be 20–30 nM. We further examine the effect of cannabinoid receptor signaling on extracellular glutamate, and find no evidence for increased extracellular glutamate upon cannabinoid receptor agonist application.
Evaluation of polyester high-tenacity fabric and carbon nanotube reinforcements for improving flexural response in concrete beams
Authors: El-Feky M. S. , Badawy Amr H. , Seddik Khaled Mohamed , Yahia Sarah 
Published: November 2024

AbstractThis research scrutinized the effectiveness of utilizing polyester high tenacity fabrics to enhance the functionality of concrete panels. Two distinct woven fabrics with comparable strength resistance were fabricated and assessed. Concrete beams were compared in their original form and those reinforced with woven fabrics, along with beams reinforced with carbon nanotubes (CNTs) (B, BC2, BC4, BS1, and BS2). Results indicated that the textile-reinforced concrete panels displayed notably greater energy absorption capabilities post-failure under flexural loads in comparison to the control and CNT-reinforced panels. This enhanced performance was credited to the development of multiple cracking patterns in the textile-reinforced panels. The flexural behavior of the textile-reinforced panels was characterized by four discernible phases: a linearly increasing segment, a crack propagation phase featuring multiple cracking, a post-cracking phase with reduced stiffness, and ultimately, failure due to fabric rupture or debonding. Conversely, the control and CNT-reinforced panels exhibited a more brittle response post-initial cracking, with a limited number of cracks and reduced deformation capacity. The performance of the textile samples was largely unaffected by their specific characteristics, except for the fabric wrapping angle. The introduction of 0.04% CNTs marginally enhanced crack flexural resistance compared to the control and 0.02% CNT panels, owing to the varied distribution of CNTs within the matrix. Overall, the textile-reinforced concrete panels demonstrated superior load-bearing capacity, ductility, and energy absorption when compared to the other reinforcement techniques examined.
The ameliorative potential of platelet-rich plasma and exosome on renal ischemia/reperfusion-induced uremic encephalopathy in rats
Authors: Abdelsalam Hani M. , Samy Alaa , Mosaleem Engy E. A. , Abdelhamid Moustafa Salaheldin 
Published: November 2024

AbstractUremic Encephalopathy results from the elevation of toxins and blood-brain barrier (BBB) disruption. Renal Ischemia/Reperfusion (I/R) injury is the principal cause of acute kidney injury and brain tissue injury. The present study was crafted to estimate the restorative impact of platelet-rich plasma (PRP) and exosome injection before the reperfusion phase on the kidney following renal I/R injury and its influence on brain tissue by tracking the histopathological, biochemical, and Doppler ultrasonography alternations in both kidney and brain tissue. Forty mature male rats were divided into five groups as follows: control, I/R, PRP, exosome, and Exosome + PRP. Renal Doppler ultrasonography was traced for all rats. Serum kidney functions and acetylcholine esterase enzyme (AchE) were evaluated. Both Gamma-aminobutyric acid (GABA) and glutamate were assessed in brain tissues. The oxidative stress (malondialdehyde), anti-oxidative (glutathione and catalase), and pro-inflammatory (Tumor necrosis factor- α and interleukin-6) markers were estimated in renal tissues. Additionally, morphometric histological examination was performed in both renal and brain tissues. Both PRP and exosome-received rats exhibited a significant improvement in both serum kidney functions and AchE compared to I/R rats. There was a 3.39-fold increase in GABA and a 2.27-fold decrease in glutamate levels in the brain tissue of PRP rats compared to the I/R rats. A significant elevation (P ≤ 0.0001) of glutathione and catalase besides a significant reduction in the expression of TNF-α and IL-6 was observed in renal tissue compared to I/R rats. A significant severe reduction (P < 0.0001) in the number of Purkinje cells, pyramidal cells in the cerebellar cortex, and the CA1 region in the hippocampus was observed in I/R rats which was significantly alleviated by both PRP and exosome. Furthermore, there was a significant improvement in Doppler parameters. PRP exerted a significant superior impact on the restoration of kidney functions and repairing uremic-induced damage in brain tissue.
Description of the mitochondrial genomes of Sichuan Tetrastes sewerzowi (Galliformes: Tetraonidae) and phylogenetic relationship
Authors: Luo Xuequn , Zhou Hang , Li Ao , Huang Zhaoxia , Zhang Wenping , Du Shizhang , Hua Donglai 
Published: November 2024

AbstractThe Chinese Grouse (Tetrastes sewerzowi), a rare endemic bird in China, belongs to the Galliformes order and Tetraonidae family. Despite extensive research, subspecies classification remains inadequate, especially in Pingwu, Sichuan. This study sequenced the mitochondrial genome of T. sewerzowi in Wanglang Nature Reserve, Sichuan, analyzed its structural features, and reconstructed phylogenetic relationships within Tetraonidae. The results indicate that the high conservation in mitochondrial genome structure and sequence between the T. sewerzowi from Wanglang, Sichuan and Lianhuashan, Gansu indicates that they may belong to the same subspecies. Furthermore, we observed significant differences in the length of the mitochondrial control region between T. sewerzowi and Tetrastes bonasia. Based on the phylogenetic tree constructed using a comprehensive mitochondrial dataset, we propose the reclassification of the Hazel Grouse genus into two independent genera: Tetrastes and Bonasa. This study is the first to sequence the mitochondrial genome of T. sewerzowi from Sichuan and compare it with populations from Gansu, providing important insights into the distribution pattern of T. sewerzowi subspecies and facilitating the formulation of effective conservation strategies.
Automated image acquisition and analysis of graphene and hexagonal boron nitride from pristine to highly defective and amorphous structures
Authors: Propst Diana , Joudi Wael , Längle Manuel , Madsen Jacob , Kofler Clara , Mayer Barbara M. , Lamprecht David , Mangler Clemens , Filipovic Lado , Susi Toma , Kotakoski Jani 
Published: November 2024

AbstractDefect-engineered and even amorphous two-dimensional (2D) materials have recently gained interest due to properties that differ from their pristine counterparts. Since these properties are highly sensitive to the exact atomic structure, it is crucial to be able to characterize them at atomic resolution over large areas. This is only possible when the imaging process is automated to reduce the time spent on manual imaging, which at the same time reduces the observer bias in selecting the imaged areas. Since the necessary datasets include at least hundreds if not thousands of images, the analysis process similarly needs to be automated. Here, we introduce disorder into graphene and monolayer hexagonal boron nitride (hBN) using low-energy argon ion irradiation, and characterize the resulting disordered structures using automated scanning transmission electron microscopy annular dark field imaging combined with convolutional neural network-based analysis techniques. We show that disorder manifests in these materials in a markedly different way, where graphene accommodates vacancy-type defects by transforming hexagonal carbon rings into other polygonal shapes, whereas in hBN the disorder is observed simply as vacant lattice sites with very little rearrangement of the remaining atoms. Correspondingly, in the case of graphene, the highest introduced disorder leads to an amorphous membrane, whereas in hBN, the highly defective lattice contains a large number of vacancies and small pores with no indication of amorphisation. Overall, our study demonstrates that combining automated imaging and image analysis is a powerful way to characterize the structure of disordered and amorphous 2D materials, while also illustrating some of the remaining shortcomings with this methodology.
Identification of sentinel lymph node macrometastasis in breast cancer by deep learning based on clinicopathological characteristics
Authors: Zhang Daqu , Svensson Miriam , Edén Patrik , Dihge Looket 
Published: November 2024

AbstractThe axillary lymph node status remains an important prognostic factor in breast cancer, and nodal staging using sentinel lymph node biopsy (SLNB) is routine. Randomized clinical trials provide evidence supporting de-escalation of axillary surgery and omission of SLNB in patients at low risk. However, identifying sentinel lymph node macrometastases (macro-SLNMs) is crucial for planning treatment tailored to the individual patient. This study is the first to explore the capacity of deep learning (DL) models to identify macro-SLNMs based on preoperative clinicopathological characteristics. We trained and validated five multivariable models using a population-based cohort of 18,185 patients. DL models outperform logistic regression, with Transformer showing the strongest results, under the constraint that the sensitivity is no less than 90%, reflecting the sensitivity of SLNB. This highlights the feasibility of noninvasive macro-SLNM prediction using DL. Feature importance analysis revealed that patients with similar characteristics exhibited different nodal status predictions, indicating the need for additional predictors for further improvement.
Event-based high-resolution neutron image formation analysis using intensified CMOS cameras
Authors: Gustschin Alex , Han Yiyong , Losko Adrian , Wolfertz Alexander , Hussey Daniel S. , Szentmiklósi László , Kis Zoltán , Trtik Pavel , Boillat Pierre , Kaestner Anders , Strobl Markus , Tengattini Alessandro , Helfen Lukas , Schulz Michael 
Published: November 2024

AbstractWe present a versatile optical setup for high-resolution neutron imaging with an adaptable field of view and magnification that can resolve individual neutron absorption events with an image intensifier and a CMOS camera. Its imaging performance is characterized by evaluating the resolution limits of the individual optical components and resulting design aspects are discussed. Neutron radiography measurements of a Siemens star pattern were performed in event mode acquisition comparing two common high-resolution neutron scintillators, crystalline Gadolinium Gallium Garnet (GGG) and powdered Gadolinium Oxysulfide (GOS). An analysis of the light signature caused by neutron absorption events is performed and some resulting issues for both GGG and GOS regarding optical system design are addressed. Both scintillators reach similar resolution (4–5 $${\upmu }$$
μ
m) in event mode acquisition despite different light emission characteristics. The findings suggest that, in the case of GOS, the resolution is limited by the size of the light clusters which in turn originate from the photon scattering at the boundaries of the powder particles comprising it, while with GGG the lower light conversion efficiency makes it challenging to collect enough photons to trigger sufficient signal amplification in the image intensifier. Overall, the proposed event-based evaluation of scintillators allows for quantifying and optimizing various design parameters, which is much more complex than adopting conventional methods based on integrated images.
Synchronization-based fusion of EEG and eye blink signals for enhanced decoding accuracy
Authors: Alyan Emad , Arnau Stefan , Reiser Julian Elias , Wascher Edmund 
Published: November 2024

AbstractDecoding locomotor tasks is crucial in cognitive neuroscience for understanding brain responses to physical tasks. Traditional methods like EEG offer brain activity insights but may require additional modalities for enhanced interpretative precision and depth. The integration of EEG with ocular metrics, particularly eye blinks, presents a promising avenue for understanding cognitive processes by combining neural and ocular behaviors. However, synchronizing EEG and eye blink activities poses a significant challenge due to their frequently inconsistent alignment. Our study with 35 participants performing various locomotor tasks such as standing, walking, and transversing obstacles introduced a novel methodology, pcEEG+, which fuses EEG principal components (pcEEG) with aligned eye blink data (syncBlink). The results demonstrated that pcEEG+ significantly improved decoding accuracy in locomotor tasks, reaching 78% in some conditions, and surpassed standalone pcEEG and syncBlink methods by 7.6% and 22.7%, respectively. The temporal generalization matrix confirmed the consistency of pcEEG+ across tasks and times. The results were replicated using two driving simulator datasets, thereby confirming the validity of our method. This study demonstrates the efficacy of the pcEEG+ method in decoding locomotor tasks, underscoring the importance of temporal synchronization for accuracy and offering a deeper insight into brain activity during complex movements.
Habitat selection and influence on hunting success in female Australian fur seals
Authors: Bartes Saia Nahir , Monk Jacquomo , Jenkins Chris , Hindell Mark A. , Costa Daniel P. , Arnould John P. Y. 
Published: November 2024

AbstractDetermining the factors influencing habitat selection and hunting success in top predators is crucial for understanding how these species may respond to environmental changes. For marine top predators, such factors have been documented in pelagic foragers, with habitat use and hunting success being linked to chlorophyll-a concentrations, sea surface temperature and light conditions. In contrast, little is known about the determinants of benthic marine predators. The Australian fur seal (Arctocephalus pusillus doriferus) is a benthic-diving forager that has a breeding and foraging distribution largely restricted to Bass Strait, the shallow (max. depth 80 m) continental shelf region between the Australian mainland and Tasmania. The species forages mostly on benthic prey and represents the greatest resident marine predator biomass in south-eastern Australia. The region is also one of the world’s fastest-warming marine areas and oceanographic changes are influencing shifts in prey distribution and abundance. In the present study, GPS-derived locations of benthic dives (n = 288,449) and dive behaviour metrics were used to determine seafloor habitat selection and factors influencing hunting success in 113 lactating adult females from Kanowna Island during the winters of 2006–2021. Individuals non-randomly selected foraging habitats comprised of deeper, steeper sloped, muddy-sandy areas with less gravel and highly disturbed regions (P < 0.01). Hunting success was greatest in shallower rocky reefs (< 30 m) and deep areas (> 40 m) characterised by moderate presence of gravel (25–50%) and substantial rock composition (50–75%) on the seabed. These findings suggest that habitat use and hunting success in adult female Australian fur seals could be impacted by predicted oceanographic changes, such as rising temperature, altered currents and waves which may modify seafloor characteristics and benthic communities.
Measuring the radiation hardness of terahertz devices for space applications
Authors: He Yuan-Zhi , Ma Chen-Sheng , Yin Hao 
Published: November 2024

AbstractThe application of terahertz technology in space is frontier for the development of 6G technologies. Terahertz transceiver devices based on gallium arsenide Schottky barrier diodes (GaAs SBDs) have the characteristics of small size, light weight and low power consumption, making them suitable for application on spacecraft. However, there is currently a lack of experimental assessments on their space adaptability. Here, we study the radiation hardness of terahertz devices to determine their adaptability in complex space environments. We exposed GaAs SBDs and terahertz multipliers as typical terahertz devices to gamma rays and protons. The experimental results showed that the terahertz devices exhibited good tolerance to protons, but prolonged exposure to gamma rays could significantly increase the leakage current of the GaAs SBDs and alter its C-V characteristics, leading to the failure of the terahertz multiplier. Nevertheless, the terahertz devices maintained a good level of radiation hardness, making them highly suitable for use in Low Earth Orbit (LEO) satellites. The comparison between the results of proton and gamma ray tests indicated that the terahertz devices exhibited high inherent radiation hardness against displacement damage but were more sensitive to ionization damage, requiring higher shielding requirements.
Deep learning to predict cardiovascular mortality from aortic disease in heavy smokers
Authors: Rau Alexander , Michel Lea , Wilhelm Ben , Raghu Vineet K. , Reisert Marco , Jung Matthias , Kellner Elias , Schlett Christopher L. , Aerts Hugo J. W. L. , Lu Michael T. , Bamberg Fabian , Weiss Jakob 
Published: November 2024

AbstractAortic angiopathy is a common manifestation of cardiovascular disease (CVD) and may serve as a surrogate marker of CVD burden. While the maximum aortic diameter is the primary prognostic measure, the potential of other features to improve risk prediction remains uncertain. This study developed a deep learning framework to automatically quantify thoracic aortic disease features and assessed their prognostic value in predicting CVD mortality among heavy smokers. Using non-contrast chest CTs from the National Lung Screening Trial (NLST), aortic features quantified included maximum diameter, volume, and calcification burden. Among 24,770 participants, 440 CVD deaths occurred over a mean 6.3-year follow-up. Aortic calcifications and volume were independently associated with CVD mortality, even after adjusting for traditional risk factors and coronary artery calcifications. These findings suggest that deep learning-derived aortic features could improve CVD risk prediction in high-risk populations, enabling more personalized prevention strategies.
Study on two‐stage optimal scheduling of DC distribution networks considering flexible load response
Authors: Chen Lei , Yang Man , Jiang Yuqi , Zheng Shencong , Li Yifei , You Xiaoyan , Chen Hongkun 
Published: November 2024

AbstractDue to the aim of developing sustainable energy systems, promoting the large‐scale accommodation of distributed renewable energy sources (DRESs) and flexible loads in DC distribution networks (DCDNs) is significant. The uncertainty of DRESs and the insufficient use of flexible loads pose a considerable challenge to the economical and safe operation of DCDNs. To address the challenge, this paper puts forward a two‐stage optimal scheduling model for the DCDNs considering flexible load response. The proposed model realises joint economic optimisation and reactive power optimisation, which is solved by the hybrid NSGAII‐MOPSO algorithm and the CPLEX. The performance of the proposed model in the modified Institute of Electrical and Electronics Engineers 33‐node system with the DCDNs is validated under different scenarios. The hybrid NSGAII‐MOPSO performs better in obtaining the Pareto front than the NSGA‐II and MOPSO individually. Compared to the traditional scheduling model, the proposed model can realise the power coordination of the flexible loads and energy storage systems to reduce the negative impact of uncertainty of DRESs while decreasing operating costs and carbon emissions by 3.94% and 36.4%. In addition, the proposed model can alleviate the network losses and ensure the node voltage for the DCDNs. Hence, the efficiency of the proposed model has been confirmed.