bluuebunny/crossref_metadata_2025_split huggingface dataset:
Engineered heart muscle allografts for heart repair in primates and humans
Authors: Jebran Ahmad-Fawad , Seidler Tim , Tiburcy Malte , Daskalaki Maria , Kutschka Ingo , Fujita Buntaro , Ensminger Stephan , Bremmer Felix , Moussavi Amir , Yang Huaxiao , Qin Xulei , Mißbach Sophie , Drummer Charis , Baraki Hassina , Boretius Susann , Hasenauer Christopher , Nette Tobias , Kowallick Johannes , Ritter Christian O. , Lotz Joachim , Didié Michael , Mietsch Mathias , Meyer Tim , Kensah George , Krüger Dennis , Sakib Md Sadman , Kaurani Lalit , Fischer Andre , Dressel Ralf , Rodriguez-Polo Ignacio , Stauske Michael , Diecke Sebastian , Maetz-Rensing Kerstin , Gruber-Dujardin Eva , Bleyer Martina , Petersen Beatrix , Roos Christian , Zhang Liye , Walter Lutz , Kaulfuß Silke , Yigit Gökhan , Wollnik Bernd , Levent Elif , Roshani Berit , Stahl-Henning Christiane , Ströbel Philipp , Legler Tobias , Riggert Joachim , Hellenkamp Kristian , Voigt Jens-Uwe , Hasenfuß Gerd , Hinkel Rabea , Wu Joseph C. , Behr Rüdiger , Zimmermann Wolfram-Hubertus 
Published: January 2025

Abstract
Cardiomyocytes can be implanted to remuscularize the failing heart1–7. Challenges include sufficient cardiomyocyte retention for a sustainable therapeutic impact without intolerable side effects, such as arrhythmia and tumour growth. We investigated the hypothesis that epicardial engineered heart muscle (EHM) allografts from induced pluripotent stem cell-derived cardiomyocytes and stromal cells structurally and functionally remuscularize the chronically failing heart without limiting side effects in rhesus macaques. After confirmation of in vitro and in vivo (nude rat model) equivalence of the newly developed rhesus macaque EHM model with a previously established Good Manufacturing Practice-compatible human EHM formulation8, long-term retention (up to 6 months) and dose-dependent enhancement of the target heart wall by EHM grafts constructed from 40 to 200 million cardiomyocytes/stromal cells were demonstrated in macaques with and without myocardial infarction-induced heart failure. In the heart failure model, evidence for EHM allograft-enhanced target heart wall contractility and ejection fraction, which are measures for local and global heart support, was obtained. Histopathological and gadolinium-based perfusion magnetic resonance imaging analyses confirmed cell retention and functional vascularization. Arrhythmia and tumour growth were not observed. The obtained feasibility, safety and efficacy data provided the pivotal underpinnings for the approval of a first-in-human clinical trial on tissue-engineered heart repair. Our clinical data confirmed remuscularization by EHM implantation in a patient with advanced heart failure.
Modulation of biological activities in adipose derived stem cells by histone deacetylation
Authors: Abdallah Sallam , Tabebi Mouna , Qanadilo Sawsan , Ali Neserin , Wang Jing , D’Arcy Pádraig , Zhong Wen , Sjoberg Folke , Elmasry Moustafa , El-Serafi Ahmed 
Published: January 2025

Abstract
Difficult-to-heal wounds management accounts for about 4% of healthcare costs, highlighting the need for innovative solutions. Extracellular signals drive cell proliferation during tissue regeneration, while epigenetic mechanisms regulate stem cell homeostasis, differentiation, and skin repair. Exploring epigenetic regulation in adipose-derived stem cells (ADSCs) holds promise for improving skin injury treatments. We investigated the effects of histone deacetylase inhibitor (SAHA) on ADSCs to better understand its cellular and molecular impacts. ADSCs were treated with SAHA for 72 h, showing no change in cell viability at the studied concentrations. However, the expression of histone deacetylase decreased at 1000 nM, while the cell proliferation marker Ki-67 increased after SAHA treatment, as confirmed by immunofluorescence. CCND1 gene expression increased, whereas protein expression of the proliferating cell nuclear antigen (PCNA) decreased. Cell cycle analysis showed an increase in G2 phase in SAHA-treated cells. Microarray analysis revealed 74 upregulated and 40 downregulated differentially expressed genes, including upregulation of P53 targets, CDKN1A and MDM2. Proteomic analysis identified 631 upregulated and 823 downregulated proteins compared to the vehicle. Pathway enrichment analysis showed cell cycle, ATP-dependent chromatin remodeling and DNA processes were among the affected pathways. This study suggests SAHA modulates ADSCs’ biological processes, highlighting its potential for skin regeneration.
Transforming CCTV cameras into NO2 sensors at city scale for adaptive policymaking
Authors: Ibrahim Mohamed R. , Lyons Terry 
Published: January 2025

Abstract
Air pollution in cities, especially NO2, is linked to numerous health problems, ranging from mortality to mental health challenges and attention deficits in children. While cities globally have initiated policies to curtail emissions, real-time monitoring remains challenging due to limited environmental sensors and their inconsistent distribution. This gap hinders the creation of adaptive urban policies that respond to the sequence of events and daily activities affecting pollution in cities. Here, we demonstrate how city CCTV cameras can act as a pseudo-NO2 sensors. Using a predictive graph deep model, we utilised traffic flow from London’s cameras in addition to environmental and spatial factors, generating NO2 predictions from over 133 million frames. Our analysis of London’s mobility patterns unveiled critical spatiotemporal connections, showing how specific traffic patterns affect NO2 levels, sometimes with temporal lags of up to 6 h. For instance, if trucks only drive at night, their effects on NO2 levels are most likely to be seen in the morning when people commute. These findings cast doubt on the efficacy of some of the urban policies currently being implemented to reduce pollution. By leveraging existing camera infrastructure and our introduced methods, city planners and policymakers could cost-effectively monitor and mitigate the impact of NO2 and other pollutants.
scATAC-seq generates more accurate and complete regulatory maps than bulk ATAC-seq
Authors: Gur E. Ravza , Hughes Jim R. 
Published: January 2025

Abstract
Bulk ATAC-seq assays have been used to map and profile the chromatin accessibility of regulatory elements such as enhancers, promoters, and insulators. This has provided great insight into the regulation of gene expression in many cell types in a variety of organisms. To date, ATAC-seq has most often been used to provide an average evaluation of chromatin accessibility in populations of cells. The development of a single cell approach (scATAC-seq) assay enables researchers to evaluate chromatin accessibility in individual cells and identify sub-groups in mixed populations of cells. To investigate the full potential of single-cell epigenomic data, we have comprehensively compared the information derived from bulk ATAC-seq and scATAC-seq in populations of cells. We found that the chromatin architecture signal is the same using bulk ATAC-seq and scATAC-seq to analyse aliquots of the same cell population. However, scATAC-seq provides substantially higher data quality compared to bulk ATAC-seq improving the sensitivity to detect relatively weak, but functionally important ATAC-seq signals. Furthermore, we found that scATAC-seq identified differences in what was previously assumed to be a homogenous population of cells. Finally, we determined the number of cells required to generate aggregated open chromatin profiles from single cells and to identify biologically meaningful clusters after pseudo-bulking of data. This study illustrates the added value of using scATAC-seq rather than bulk ATAC-seq in evaluating both homogeneous and heterogeneous populations of cells. This paper provides a comprehensive guide on the benefits of using scATAC-seq data to study gene regulation.
2D wrinkle assisted zigzag plasmonic chains for isotropic SERS enhancement
Authors: Yu Ziwen , Sarkar Swagato , Seçkin Sezer , Sun Ningwei , Ghosh Anik Kumar , Wießner Sven , Zhou Ziwei , Fery Andreas 
Published: January 2025

Abstract
Template-assisted colloidal self-assembly has gained significant attention due to its flexibility and versatility. By precisely controlling the shape of the template, it is possible to achieve custom-designed nanoparticle assemblies. However, a major challenge remains in fabricating these templates over large areas at a low cost. Recently, one-dimensional (1D) nano-wrinkle structures have been effectively used for the linear assembly of single-chain or multi-chain nanoparticles, which feature abundant interparticle nanogaps that facilitate efficient plasmonic coupling. To further enhance these assemblies by incorporating diffraction modes, we develop two-dimensional (2D) zigzag wrinkle structures that successfully assemble nanoparticles into plasmonic zigzag chains. Micro spectral measurements and FDTD simulations reveal that zigzag assemblies of plasmonic nanoparticle chains offer isotropic behavior and exhibit stronger plasmonic coupling compared to 1D assemblies, which could be highly beneficial for sensing applications. Due to the responsive PANI shell encapsulating the gold nanoparticles, this 2D zigzag assembly enables flexible tuning of plasmonic resonance under pH regulation.
Revolutionizing load harmony in edge computing networks with probabilistic cellular automata and Markov decision processes
Authors: Sahu Dinesh , Nidhi  , Chaturvedi Rajnish , Prakash Shiv , Yang Tiansheng , Rathore Rajkumar Singh , Wang Lu , Tahir Sabeen , Bakhsh Sheikh Tahir 
Published: January 2025

Abstract
In general, edge computing networks are based on a distributed computing environment and hence, present some difficulties to obtain an appropriate load balancing, especially under dynamic workload and limited resources. The conventional approaches of Load balancing like Round-Robin and Threshold-based load balancing fails in scalability and flexibility issues when applied to highly variable edge environments. To solve the problem of how to achieve steady-state load balance and provide dynamic adaption to edge networks, this paper proposes a new framework that using PCA and MDP. Taking advantage of the stochasticity of PCA classification our model describes interactions between neighboring nodes in terms of a local load thus allowing for a distributed, self-organizing approach to load balancing. The MDP framework then determines each node’s decision-making with the focus on load offloading policies that are aligned with rewards that promote per node balance and penalties for offloading a larger load than it can handle.These models are then incorporated into our proposed PCA-MDP system to achieve dynamic load balancing with low variability in resource usage among nodes. By conducting a large number of experiments, we prove that the proposed PCA-MDP model yields a higher efficiency in the distribution of the load, higher stabilities of the reward function, and a faster convergence speed compared to the existing approaches. Key performance parameters, such as load variance, convergence time, and scalability, validate the robustness of the proposed model. Besides optimizing resource exploitation, load harmony in edge computing networks helps provide efficient work progression and minimize latency, thereby contributing to the advancement of the field with respect to real-time applications such as self-driving vehicles and the Internet of Things. The presented work offers an excellent foundation for the next-generation edge-computing load-balancing solution that can be easily scaled up.
Green communication for OverGNN enabled heterogeneous ultra‐dense networks
Authors: Zeng Jun , Lin Sisi , Ai Yuhan , Wan Guo , Luo Wei , Chen Qimei 
Published: January 2025

AbstractWith the development of wireless communications, heterogeneous ultra‐dense networks (HUDNs) have emerged to meet the requirements of massive connectivity, high data rate, and low latency in the 5G era. Nevertheless, HUDN usually leads to a high‐complexity and non‐convex NP‐hard energy‐efficient resource allocation problem. Therefore, A novel heterogeneous Graph neural network (GNN) with high‐dimensional computation structure (namely OverGNN) is proposed for the power allocation problem in this work. Particularly, OverGNN enabled nodes directly interact with high‐order neighbours and extract abundant graph topological information, which can facilitate effective feature aggregation among nodes as well as alleviate the over‐smoothing problem. Based on this fact, an efficient message passing scheme for user equipments under the same base station is developed to approximate the optimal power allocation strategy for maximizing system energy efficiency. In addition, an unsupervised approach is proposed to train the GNN model that can reduce the cost of dataset collection and enhance the scalability of the proposed method. Numerical results verify the effectiveness of the proposed OverGNN and demonstrate its advantages over the benchmarks.
High-speed imaging of titanium cathode spots in a high-current vacuum arc thruster
Authors: Michaux E , Julien J , Mazouffre S 
Published: January 2025

Abstract
The apparent movement of plasma emission sites across the cathode surface of a 30 W high-current vacuum arc thruster is examined using high-speed imaging. Images captured at a frame rate of 450 000 fps reveal the temporal evolution of the cathode spot distribution over the course of a single current pulse. Films were recorded with a titanium cathode and a copper anode. A comparison with the discharge current shows a locally high current density, on the order of 109 A m−2. Quantitative analysis on clustering of cathode spots, and the mapping of their trajectory over the cathode surface offer insights into the cathode erosion dynamics.
Analytical modeling and TCAD simulation of surface potential and drain current for pocket doped negative capacitance field-effect transistor
Authors: Malvika  , Talukdar Jagritee , Choudhuri Bijit , Mummaneni Kavicharan 
Published: January 2025

Abstract
In this work, a 2D analytical model for a Highly Doped Double Pocket Double Gate NCFET (HDDP-DG-NCFET) device has been developed. The proposed device HDDP-DG-NCFET comprises double gate, ferroelectric material, highly doped pockets at the edge of source-channel and drain-channel junction and spacers. The double gate and the silicon doped HfO2 ferroelectric material provides better electrostatic control over the channel and improved subthreshold swing, respectively. The body is made more confined against the junction walls by incorporating a thin, highly doped region of a similar doping, which serves to reduce the extent of depletion regions that further increases the channel length for gate control and reduces short channel effects. Further, the device consists of spacers which increase the ON current. The HDDP-DG-NCFET provides I

ON
, I

OFF
, I

ON
/I

OFF
ratio and SS of 2.1 × 10−3 A/μm, 2.65 × 10−13 A/μm, 0.79 × 1010 and 25.5 mV/decade, respectively. The framework of the model is derived from the Poisson’s equation, LK equation and solved utilizing appropriate boundary conditions. The developed model is projected for several device variables, including electric field, surface potential and drain current. Afterward, to verify the model’s accuracy, Sentaurus TCAD has been utilized for device simulation, and a good agreement with 85–95% precision is witnessed. The comprehensive and structured analysis of device is provided by examining variations in dimensions of device, ferroelectric material and oxide materials. The presented analysis shows that, the proposed HDDP-DG-NCFET device has significant potential for low power applications.
Model-agnostic assessment of dark energy after DESI DR1 BAO
Authors: Dinda Bikash R. , Maartens Roy 
Published: January 2025

Abstract
Baryon acoustic oscillation measurements by the Dark Energy Spectroscopic Instrument (Data
Release 1) have revealed exciting results that show evidence for dynamical dark energy at
∼ 3σ when combined with cosmic microwave background and type Ia supernova
observations. These measurements are based on the w
0
w

a
CDM model of dark energy. The evidence
is less in other dark energy models such as the wCDM model. In order to avoid imposing a dark
energy model, we reconstruct the distance measures and the equation of the state of dark energy
independent of any dark energy model and driven only by observational data. Our results show that
the model-agnostic (in terms of late-time models) evidence for dynamical dark energy from DESI is
not significant. Our analysis also provides model-independent constraints on cosmological
parameters such as the Hubble constant and the matter-energy density parameter at
present. Although we used CMB distance priors (not full CMB data) from a ΛCDM early-time
model, our results remain largely similar for other cosmological models, provided that these
models do not differ significantly from the standard model.
Decoupled gravitational wave equations in spherical symmetry from curvature wave equations
Authors: Mukkamala Gowtham Rishi , Pereñiguez David 
Published: January 2025

Abstract
Black hole perturbation theory on spherically symmetric backgrounds has been instrumental in establishing various aspects about the gravitational dynamics close to black holes, and continues to be an interesting avenue to confront current challenges in gravitational physics. In this paper, we present an approach to perturbation theory in spherical symmetry that addresses simultaneously some conceivably inconvenient aspects of the traditional methods. In particular, focusing on Schwarzschild's background we are able to derive a decoupled wave equation, for a single complex variable, by simply computing one component of the curvature wave equation satisfied by a complex self-dual version of the Riemann tensor. The real and imaginary parts of the variable consist only of even and odd pieces of the metric fluctuation, respectively, and both satisfy the Regge-Wheeler equation. Besides providing a systematic derivation of decoupled equations, an immediate corollary of our results is the isospectrality between even and odd sectors. We conclude by discussing potential extensions of our formalism to include matter and higher orders in perturbation theory.
High power, high beam quality and narrow linewidth diode-end-pumped Nd:YAG seed laser at 1319 nm for a sodium guide star laser system
Authors: Shen Yu , Wen Ning , Wen Ya , Gao Hongwei , Bian Qi , Bo Yong , Peng Qinjun 
Published: January 2025

Abstract
A high power, high beam quality and narrow linewidth Nd:YAG 1319 nm pulsed seed laser for sodium beacon is demonstrated, which was dual-end-pumped by the low quantum loss 885 nm quasi-continuous wave laser diodes (LDs). The resonator was based on a ring convex–convex structure with a dual-rod configuration. Adjusting the effective pump size in the crystal and the temperature of the LDs to realize the optimal size ratio of the pump beam and laser mode was analyzed finely. An output power of up to 25 W at 1319 nm has been obtained, operating at 800 Hz with a pulse duration of 140 μs, corresponding to a high optical-to-optical conversion efficiency of 18%. The average beam quality factor of M
2 was measured to be 1.25. To the best of our knowledge, this is both the highest output power and the highest beam quality at 1319 nm for the 885 nm LDs end-pumped with the pulse oscillator. This result provides a 1319 nm seed laser for a sodium guide star laser system, with high quality (high power, high beam quality and narrow linewidth), and also better stability.
Generalized hetero-associative neural networks
Authors: Agliari Elena , Alessandrelli Andrea , Barra Adriano , Centonze Martino Salomone , Ricci-Tersenghi Federico 
Published: January 2025

Abstract
Auto-associative neural networks (e.g. the Hopfield model implementing the standard Hebbian prescription) serve as a foundational framework for pattern recognition and associative memory in statistical mechanics. However, their hetero-associative counterparts, though less explored, exhibit even richer computational capabilities. In this work, we examine a straightforward extension of Kosko’s bidirectional associative memory, namely a three-directional associative memory, that is a tripartite neural network equipped with generalized Hebbian weights. Through both analytical approaches (using replica-symmetric statistical mechanics) and computational methods (via Monte Carlo simulations), we derive phase diagrams within the space of control parameters, revealing a region where the network can successfully perform pattern recognition as well as other tasks. In particular, it can achieve pattern disentanglement, namely, when presented with a mixture of patterns, the network can recover the original patterns. Furthermore, the system is capable of retrieving Markovian sequences of patterns and performing generalized frequency modulation.
Convergence properties of Markov models for image generation with applications to spin-flip dynamics and to diffusion processes
Authors: Monthus Cécile 
Published: January 2025

Abstract
In the field of Markov models for image generation, the main idea is to learn how non-trivial images are gradually destroyed by a trivial forward Markov dynamics over the large time window

[
0
,
t
]

converging towards pure noise for

t
→
+
∞

, and to implement the non-trivial backward time-dependent Markov dynamics over the same time window

[
0
,
t
]

starting from pure noise at t in order to generate new images at time 0. The goal of the present paper is to analyze the convergence properties of this reconstructive backward dynamics as a function of the time t using the spectral properties of the trivial continuous-time forward dynamics for the N pixels

n
=
1
,
.
.
,
N

. The general framework is applied to two cases : (i) when each pixel n has only two states

S
n

=
±
1

with Markov jumps between them; (ii) when each pixel n is characterized by a continuous variable xn
that diffuses on an interval

]

x
−

,

x
+

[

that can be either finite or infinite.
IoT- Enabled soil moisture management for precision plant sprinkler system for organic farming
Authors: Maghirang M S , Maaño R , Santonil H S , Canela D N , De La Roca M J , Buban D J , Maaño R A 
Published: January 2025

Abstract
In daily operations related to farming, watering stands out as crucial yet labour-intensive task regardless of weather conditions. The traditional way of watering crops involves manual irrigation, which poses challenges such as inefficiency, labour intensity, and uneven water distribution that often results in overwatering in some areas leading to potential crop yield losses. It requires two important aspects: when and how much to water. With that, this project study aimed to provide an alternative way of irrigating crops and embrace automation to increase crop productivity, less water consumption, and reduce labour intensity. Farmers can benefit from automated watering systems to optimize irrigation practices. To address farming challenges, a microcontroller-based irrigation system with Arduino microcontroller, soil moisture sensors, relay modules, and water sprinkler was developed. This system aids in transitioning from traditional farming to digital farming, boosting productivity and allowing real-time monitoring via a mobile app. Additionally, it is integrated into a railing system to automate irrigation based on the crops’ watering needs. It offers a solution to labor-intensive manual watering and farming and is claimed to be effective based on the evaluation results, which yielded strong agreement in functionality, effectiveness and efficiency, with scores of, 4.24, 4.17, and 4.1 respectively. It has the potential to be an innovation in the development of more eco-friendly and sustainable agricultural practices.