bluuebunny/crossref_metadata_2025_split huggingface dataset:
A novel investigation of NANOG and POU5F1 associations in the pluripotent characterization of ES-like and epiblast cells
Authors: Roshan Mehdi Mehdinezhad , Azizi Hossein , Sojoudi Kiana 
Published: October 2024

AbstractThe transcription factors NANOG and POU5F1 (OCT4) play crucial roles in maintaining pluripotency in embryonic stem (ES) cells. While their functions have been well-studied, the specific interactions between NANOG and POU5F1 and their combined effects on pluripotency in ES-like and Epiblast cells remain less understood. Understanding these associations is vital for refining pluripotent stem cell characterization and advancing regenerative medicine. In this matter, we investigated the associations between NANOG and POU5F1 in maintaining pluripotency in ES-like and Epiblast cells and how these interactions contribute to the distinct pluripotent states of these cells. In the present paper, we examined the pattern of NANOG expression by the immunocytochemical method in embryonic stem-like (ES-like) cells and compared it with its expression pattern in embryonic stem cells (ESCs). Similarly, we examined the expression pattern of POU5F1 in ES-like cells, ESCs, and epiblast cells and compared the expression pattern of these two genes with each other. On the other hand, using Fluidigm Biomark system analysis, we compared the amount of NANOG mRNA in these three cell lines and differentiated and undifferentiated Spermatogonial stem cells in several passages. Microscopic observations indicated the cytoplasmic expression of NANOG in the considered cells; moreover, they showed a similar expression pattern of NANOG with POU5F1 in the experimented cells. It has also been suggested that the more limited the cell’s pluripotency, the lower the expression of these two genes. However, the decrease in NANOG expression is less than that of POU5F1. Fluidigm real-time RT-PCR analysis also confirmed these results. During the experimental process, protein-protein (PPI) network analysis shows a significant association of NANOG with other stem cell proteins, such as POU5F1. Our findings reveal distinct yet overlapping roles of NANOG and POU5F1 in maintaining pluripotency in ES-like and Epiblast cells. The differential binding patterns and functional interactions between these factors underscore the complexity of pluripotency regulation in different stem cell states. This study provides new insights into the molecular mechanisms governing pluripotency and highlights potential targets for enhancing stem cell-based therapies.
A scheme of combining DFO and channel estimation scheme for mobile OFDM systems
Authors: Yang Lihua , Shao Yongqi , Chang Ao , Hu Bo 
Published: October 2024

AbstractTo reduce the impact of residual Doppler frequency offset (RDFO), a joint DFO estimation and CE scheme is proposed for the OFDM systems under the high‐speed mobile environment. In the paper, the expression of interference power caused by the RDFO is first derived, and the effect of RDFO on time‐varying characteristic of channel is analysed. Then, a joint DFO and channel estimation scheme is presented. Specifically, a high‐precision DFO estimator based on the convolutional neural network with anti‐noise is firstly designed. Due to its ability to use fewer samples to adapt well to the new environments, the meta learning is adopted to estimate the time‐varying channel. Moreover, to improve the practicality of the algorithm, the non‐ideal values rather than ideal values are used as the training targets in the two neural networks. Additionally, the proposed method is only based on the received signal and does not require any pilots or training sequences, which has higher transmission efficiency compared to the existing algorithms. The research results indicate that the proposed method has good estimation performance and good practicality, and it is suitable for high‐speed mobile scenarios.
Wavelet CNN‐LSTM time series forecasting of electricity power generation considering biomass thermal systems
Authors: Buratto William Gouvêa , Muniz Rafael Ninno , Nied Ademir , Barros Carlos Frederico de Oliveira , Cardoso Rodolfo , Gonzalez Gabriel Villarrubia 
Published: October 2024

AbstractThe use of biomass as a renewable energy source for electricity generation has gained attention due to its sustainability and environmental benefits. However, the intermittent electricity demand poses challenges for optimizing electricity generation in thermal systems. Time series forecasting techniques are crucial in addressing these challenges by providing accurate predictions of biomass availability and electricity generation. Here, wavelet transform is applied for denoising, convolutional neural networks (CNN) are used to extract features of the time series, and long short‐term memory (LSTM) is applied to perform the predictions. The result of the mean absolute percentage error equal to 0.0148 shows that the wavelet CNN‐LSTM is a promising machine‐learning methodology for electricity generation forecasting. Additionally, this paper discusses the importance of model evaluation techniques and validation strategies to assess the performance of forecasting models in real‐world applications. The major contribution of this paper is related to improving forecasting using a hybrid method that outperforms other models based on deep learning. Finally, future research directions and potential advancements in time series forecasting for biomass thermal systems are outlined to foster continued innovation in sustainable energy generation.
Main lobe deceptive jamming suppression based on blind source separation and energy detection for monopulse radar
Authors: Liu Zhenhua , Liang Wei , Fu Ning , Qiao Liyan , Zhang Jun 
Published: October 2024

AbstractMain lobe deceptive jamming always causes the serious degradation of signal detection ability and angle measurement precision of monopulse radar. In recent years, the Blind Source Separation (BSS) method has been adopted to suppress the main lobe jamming. However, the separation results of BSS have the problem of amplitude ambiguity, which will cause the radar using monopulse angle measurement fail to measure the angle parameters after jamming suppression. To tackle this problem, a novel main lobe jamming suppression method is proposed based on BSS and energy detection. Firstly, the models of target echoes and jamming in the sum and difference beam receiving channels are derived. Secondly, the target echoes and interferences are separated by the Joint Approximate Diagonalisation of Eigenmatrices (JADE) algorithm, and then the unperturbed signal segments in the mixed signal are extracted by energy detection, thereby obtaining the precise ratio of the sum and difference channels to complete the angle measurement. Performance of the method was verified by numerical simulation. The results show that the proposed method can achieve interference suppression while accurately estimating the angle parameter of the target.
Investigation of Uncertainty Propagation in the Resolved Resonance Range
Authors: Sole Pierre , Jaiswal Vaibhav , Basavaraja Allannavar Aparna , Jouanne Cédric , Petillon Barthelemi , Brovchenko Mariya 
Published: October 2024

This paper presents a comparative study using the first-order formula (also called “sandwich”) and the multivariate sampling methodologies for propagating uncertainty in the resolved resonance region. A distinctive aspect of this work is the generation of random cross sections by sampling Resonance Parameters (RPs) taking in consideration their correlations provided in nuclear data libraries via the Resonance Parameter Covariance Matrix (RPCM). SCOOBY (Sampling COvariance OBservatorY), a newly developed sampling tool, is presented in this paper. This tool relies on the GAIA-2 nuclear data processing code to read and correct the RPCM and generate random cross sections.
The study compares the sandwich method that relies on sensitivity coefficients and covariance matrices, with the sampling method, which involves numerous Monte Carlo simulations with random cross sections. The comparison is tested on an ICSBEP benchmark, PU-MET-MIXED-002, chosen for its sensitivity to 239Pu cross sections. The results indicate that both methods quantify similar uncertainties, confirming the reliability of both the SCOOBY module and the GAIA-2 code.
By using the PU-MET-MIXED-002 benchmark and sampling 239Pu resonance parameters, this work demonstrates the effectiveness of these methodologies in estimating uncertainty in criticality safety calculations. The findings highlight the robustness of these approaches in uncertainty propagation, suggesting the need for further research on additional benchmarks and expanded uncertainty propagation studies.
Contexte et retombées de l’engagement de patients partenaires en milieu clinique : une étude qualitative descriptive
Authors: Caron Marie-Ève , Ouellet Nicole , Bergeron Dave 
Published: October 2024

Introduction : Engager les patients comme partenaires en milieu clinique est une approche pertinente pour répondre aux enjeux des systèmes de santé. Peu d’études explorent l’accompagnement de patients partenaires (PP) auprès de patients vivant avec une maladie chronique similaire. Objectifs : Cette étude permet de décrire le contexte d’engagement des PP dans un milieu clinique, les facteurs influençant cet engagement et ses retombées. Méthodes : Nous avons utilisé un devis qualitatif descriptif, des entrevues individuelles semi-dirigées suivi d’une analyse thématique des données. Résultats : Au total, 10 personnes ont participé aux entrevues : 1 patient, 5 PP et 4 professionnels. La majorité des PP sont engagés principalement dans le secteur des soins et certains dans l’enseignement. Les participants perçoivent plusieurs facteurs qui favorisent l’engagement des PP, comme avoir suivi un programme de formations d’éducation thérapeutique et l’acceptation de la maladie ainsi que des facteurs limitants comme la timidité et le lieu de l’intervention. L’engagement des PP favoriserait l’autogestion de la maladie en plus de briser l’isolement des patients. Conclusion : Ces données préliminaires permettent de mieux saisir l’engagement des PP en milieu clinique et les retombées positives. L’engagement de PP est un incontournable pour favoriser l’autonomie et le bien-être des personnes vivant avec une maladie chronique.
Der venöse Schleifenshunt 3
Authors: Krönung Gerhard 
Published: October 2024

ZUSAMMENFASSUNGLoop-Konfigurationen der Shuntvene sind punktionstechnisch und hämodynamisch grundsätzlich günstig und bei der Revision eines Shunts mit 2 maturierten Leitvenen oft auch gut realisierbar. Aber auch, wenn bei ungünstiger Anatomie aufwendigere gefäßchirurgische Techniken für ihre Anlage erforderlich sind, gibt es hierfür bei entsprechender Symptomatik (z. B. Steal-Syndrom oder Herzvolumenbelastung) erfolgversprechende und damit indizierte Vorgehensweisen.
Aspekte der Immunsuppression nach allogener Nierentransplantation
Authors: Matevossian Edouard , Göß Rüdiger , Garhammer Katharina , Jungwirth David , Renders Lutz , Thorban Stefan , Aßfalg Volker 
Published: October 2024

ZUSAMMENFASSUNGDie postmortale Organspende und die Vermittlung von Organen berühren fundamentale medizinische, ethische und rechtliche Facetten des gesellschaftlichen Zusammenlebens. Die Nierentransplantation stellt heute – mehr als ein halbes Jahrhundert nach ihrer Einführung in die klinische Behandlung – ein etabliertes und sicheres Therapieverfahren mit guten Langzeitergebnissen dar. Der langfristige Erfolg einer Transplantation ist multifaktoriell bedingt und hängt entscheidend von der dauerhaften und gewissenhaften Einnahme der Immunsuppressiva ab. Die optimale immunsuppressive Therapie sichert langfristig die Funktion der transplantierten Organe, indem sie die akute Transplantatabstoßung und eine chronische Transplantatdysfunktion verhindert. Die individualisierte Immunsuppression und qualifizierte Nachsorge können entscheidend dazu beitragen, die Funktionalität des Transplantats und seine Überlebenszeit relevant zu verlängern.
Neonatal Survival and Outcomes following Periviable Rupture of Membranes
Authors: Okonek Elizabeth J. , Schulz Elizabeth V. , Belzer Kira , Aden James K. , Drumm Caitlin M. 
Published: October 2024

Objective To clarify survival for infants affected by periviable prolonged preterm premature rupture of membranes (PPROM) in the military health system (MHS). To add to current literature on outcomes following expectant management, including long-term neurodevelopment outcomes.
Study Design Retrospective matched cohort review of six level 3 military neonatal intensive care units (NICUs; 2010–2020). Cases were matched 1:1 with control infants, matched by location, gender, gestational age (within 1 week), birth weight (within 300 g), and rupture of membranes (ROM) within 24 hours of delivery. Follow-up data were obtained for each infant through 48 months' corrected age or age of last documented health visit in a military treatment facility.
Results Forty-nine infants met inclusion criteria. Mean ROM for cohort infants was 20.7 weeks, with mean latency period of 34.6 days and mean gestational age at delivery of 25.7 weeks. Cohort infants had a mean birth weight of 919 g. Cohort survival to NICU discharge was 75.5 versus 77.6% of controls (p = 0.81). Statistically significant short-term outcomes: oligohydramnios or anhydramnios at delivery (p < 0.0001), pulmonary hypertension (p = 0.0003), and high-frequency ventilation (p = 0.004) were higher in cohort infants. No differences were found regarding rates of early sepsis, intraventricular hemorrhage, surgical necrotizing enterocolitis, oxygen at 36 weeks or at discharge. No statistical difference in long-term outcomes at 18 to 48 months of age or incidence of autism, cerebral palsy, attention deficit hyperactivity disorder, or asthma.
Conclusion Cohort survival to discharge in the MHS was 75.5%, higher than previously reported and not different from matched controls. Infants born after periviable PPROM should deliver at centers with access to high-frequency ventilation and ability to manage pulmonary hypertension. There was no difference in long-term neurodevelopment between the groups.
Key Points
Imaging of Body Composition
Authors: Gazzotti Silvia , Sassi Rebecca , Aparisi Gómez Maria Pilar , Guglielmi Riccardo , Vasilevska Nikodinovska Violeta , Messina Carmelo , Guglielmi Giuseppe , Bazzocchi Alberto 
Published: October 2024

AbstractBody composition is now recognized to have a major impact on health and disease. Imaging enables its analysis in an objective and quantitative way through diverse techniques such as dual-energy X-ray absorptiometry, computed tomography, magnetic resonance imaging, and ultrasonography. This review article first surveys the methodological aspects underpinning the use of these modalities to assess body composition, highlighting their strengths and limitations as well as the set of parameters that they measure and their clinical relevance. It then provides an update on the main applications of body composition imaging in current practice, with a focus on sarcopenia, obesity, lipodystrophies, cancer, and critical care. We conclude by considering the emerging role of artificial intelligence in the analysis of body composition, enabling the extraction of numerous metrics with the potential to refine prognostication and management across a number of pathologies, paving the way toward personalized medicine.
Artificial Intelligence Applications for Imaging Metabolic Bone Diseases
Authors: Isaac Amanda , Akdogan Asli Irmak , Dalili Danoob , Saber Nuran , Drobny David , Guglielmi Giuseppe , Modat Marc , Bazzocchi Alberto 
Published: October 2024

AbstractArtificial intelligence (AI) has significantly impacted the field of medical imaging, particularly in diagnosing and managing metabolic bone diseases (MBDs) such as osteoporosis and osteopenia, Paget's disease, osteomalacia, and rickets, as well as rare conditions such as osteitis fibrosa cystica and osteogenesis imperfecta. This article provides an in-depth analysis of AI techniques used in imaging these conditions, recent advancements, and their clinical applications. It also explores ethical considerations and future perspectives. Through comprehensive examination and case studies, we highlight the transformative potential of AI in enhancing diagnostic accuracy, improving patient outcomes, and contributing to personalized medicine. By integrating AI with existing imaging techniques, we can significantly enhance the capabilities of medical imaging in diagnosing, monitoring, and treating MBDs. We also provide a comprehensive overview of the current state, challenges, and future prospects of AI applications in this crucial area of health care.
Self-aggregation and microhydration mechanisms of monoethanolamine: Far-infrared identification of large-amplitude hydrogen bond libration
Authors: Yazdabadi S. Hafizi , Mihrin D. , Feilberg K. L. , Larsen R. Wugt 
Published: October 2024

The strong tendency for self-aggregation together with an intriguing mechanism for the microhydration of monoethanolamine (MEA) have been explored by low-temperature far-infrared cluster spectroscopy in doped neon “quantum” matrices at 4 K complemented by high-level quantum chemical modeling. In addition to the assignment of new mid-infrared perturbed intramolecular transitions, a distinct far-infrared transition is unambiguously assigned to the concerted large-amplitude hydrogen bond librational motion of the MEA homodimer. This observation confirms a global “head-to-head” intermolecular potential energy minimum associated with the formation of a compact doubly intermolecular OH⋯N hydrogen-bonded cyclic structure, where both monomeric intramolecular OH⋯N hydrogen bonds are broken upon complexation. By means of relative mixing ratio dependencies, dedicated annealing procedures, and selective complexation between MEA and isotopic H216O and H218O samples, distinct far-infrared transitions associated with large-amplitude intra-molecular hindered OH torsional motion and inter-molecular H2O librational (hindered c-type overall rotational) motion of the MEA monohydrate are furthermore assigned unambiguously for the first time. These spectroscopic observations reveal an intriguing metastable conformation, where H2O acts as a OH⋯O hydrogen bond donor to the hydroxy group instead of the amino group of MEA upon microhydration in the cryogenic neon environment, where the microhydration strengthens the intramolecular OH⋯N hydrogen bond of MEA due to hydrogen bond cooperativity.
Special-point approach for optical absorption coefficient calculations on two-dimensional self-assemblies of type-II perovskite quantum dots
Authors: Cabrera C I , Pérez-Álvarez R 
Published: October 2024

Abstract
All-inorganic perovskite quantum dots with the usual cubic shape have emerged as a successful and low-cost alternative to electronically functional nanomaterials motivating various fields of applications, including high-efficiency photovoltaics. Here, we present an efficient and almost analytic approach for optical absorption coefficient calculation on self-assembled perovskite quantum dot films with type-II band alignment. The approach takes advantage of the special point technique for integration over the two-dimensional Brillouin zone, which minimizes the computational cost. The set of special wave-vector points is generated using the Monkhorst and Pack method. The optical absorption spectrum for phenyl-C

6

0

-butyric acid methyl ester (PCBM)/CsPbI3 quantum dot films is computed, in good agreement with the experiment assuming a homogeneous linewidth of 50 meV and considering a ten special-point set. We show that light absorption in these systems is a cooperative optoelectronic property resulting from the quantum-mechanical coupling between perovskite nanocubes, leading to extended system states. The generality of this approach makes it suitable for calculating the optical absorption coefficient in a broad class of perovskite quantum dot systems.
Hierarchical TiO2 nanostructures sensitized with MoS2 nanosheets: an efficient and reusable heterojunction photocatalyst for sunlight-assisted degradation of organic dye pollutants
Authors: Chathambally Vijina , Damodaran Shima P 
Published: October 2024

Abstract
The present study reports the synthesis, characterization, and natural sunlight-driven photocatalytic activity of a novel heterojunction photocatalyst comprised of hierarchical rutile titanium dioxide (r-TiO2) nanostructures and 1T/2H molybdenum disulfide (MoS2) nanosheets. These components were synthesized by solvothermal methods and their effective integration was achieved by using 3- aminopropyltrimethoxysilane as coupling agent. The photocatalytic activity of r-TiO2/MoS2 nanohybrid was explored for the degradation of cationic dye methylene blue (MB), and anionic dye congo red (CR) under natural sunlight. The results reveal that the sunlight-driven photocatalytic activity of pristine r-TiO2 was drastically enhanced upon sensitization with 1T/2H MoS2 nanosheets. The nanohybrid could degrade 99% MB and 98% CR within 150 min with rate constants 25.6 × 10−3 and 13.2 × 10−3 min−1 respectively. The r-TiO2/MoS2 nanohybrid retained more than 85% of its catalytic activity even after four cycles of reuse. The scavenger test revealed that holes and hydroxyl radicals are mainly responsible for the degradation of MB and CR. The facile synthesis, outstanding catalytic activity under natural sunlight, and excellent recyclability make r-TiO2/MoS2 a promising heterojunction photocatalyst for the degradation of environmental pollutants from wastewater. The present study can provide new insights towards the development of efficient, economical and sustainable photocatalysts for harnessing renewable solar energy for environmental remediation applications.
A novel nonlinear SAZIQHR epidemic transmission model: mathematical modeling, simulation, and optimal control
Authors: Kumar Abhishek , Tanvi  , Aggarwal Rajiv 
Published: October 2024

Abstract
This study presents a seven-compartmental nonlinear epidemic model to explore the dynamics and control of the epidemic. The model extends the traditional susceptible-infected-recovered (SIR) framework by including additional compartments for aware, infodemic, hospitalized, and quarantined populations. It also incorporates three Holling type II saturated nonlinear incidence rates to better represent the disease transmission process. The ‘infodemic population’ refers to those spreading misinformation about the disease, its spread, control, and treatment. The study examines disease dynamics in the absence of a vaccine and identifies two key equilibria: the disease-free equilibrium (DFE) and the endemic equilibrium (EE). It is found that the DFE is locally asymptotically stable when the basic reproduction number (

R

0

) is below one and becomes unstable when

R

0

exceeds one. The model’s behavior at

R

0

=
1

is analyzed using center manifold theory, revealing a forward bifurcation. Additionally, the existence of a positive endemic equilibrium is confirmed for

R

0

>
1
,

with no backward bifurcation observed when

R

0

≤
1
.

The EE is also shown to be locally asymptotically stable when

R

0

is greater than one. Furthermore, the study formulates and mathematically analyzes an optimal control problem. Finally, numerical simulations are presented to support the analytical results.