bluuebunny/crossref_metadata_2025 huggingface dataset:
Vascular remodelling in cardiovascular diseases: hypertension, oxidation, and inflammation
Authors: Totoń-Żurańska Justyna , Mikolajczyk Tomasz P. , Saju Blessy , Guzik Tomasz J.
Published: June 2024

Optimal vascular structure and function are essential for maintaining the physiological functions of the cardiovascular system. Vascular remodelling involves changes in vessel structure, including its size, shape, cellular and molecular composition. These changes result from multiple risk factors and may be compensatory adaptations to sustain blood vessel function. They occur in diverse cardiovascular pathologies, from hypertension to heart failure and atherosclerosis. Dynamic changes in the endothelium, fibroblasts, smooth muscle cells, pericytes or other vascular wall cells underlie remodelling. In addition, immune cells, including macrophages and lymphocytes, may infiltrate vessels and initiate inflammatory signalling. They contribute to a dynamic interplay between cell proliferation, apoptosis, migration, inflammation, and extracellular matrix reorganisation, all critical mechanisms of vascular remodelling. Molecular pathways underlying these processes include growth factors (e.g., vascular endothelial growth factor and platelet-derived growth factor), inflammatory cytokines (e.g., interleukin-1β and tumour necrosis factor-α), reactive oxygen species, and signalling pathways, such as Rho/ROCK, MAPK, and TGF-β/Smad, related to nitric oxide and superoxide biology. MicroRNAs and long noncoding RNAs are crucial epigenetic regulators of gene expression in vascular remodelling. We evaluate these pathways for potential therapeutic targeting from a clinical translational perspective. In summary, vascular remodelling, a coordinated modification of vascular structure and function, is crucial in cardiovascular disease pathology.
Lexical Morphology as a Source of Risk and Resilience for Learning to Read With Dyslexia: An fNIRS Investigation
Authors: Eggleston Rachel L. , Marks Rebecca A. , Sun Xin , Yu Chi-Lin , Zhang Kehui , Nickerson Nia , Hu Xiaosu , Caruso Valeria , Kovelman Ioulia
Published: June 2024

Purpose:

We examined the neurocognitive bases of lexical morphology in children of varied reading abilities to understand the role of meaning-based skills in learning to read with dyslexia.

Method:

Children completed auditory morphological and phonological awareness tasks during functional near-infrared spectroscopy neuroimaging. We first examined the relation between lexical morphology and phonological processes in typically developing readers (Study 1,
N
= 66,
M
age
= 8.39), followed by a more focal inquiry into lexical morphology processes in dyslexia (Study 2,
N
= 50,
M
age
= 8.62).

Results:

Typical readers exhibited stronger engagement of language neurocircuitry during the morphology task relative to the phonology task, suggesting that morphological analyses involve synthesizing multiple components of sublexical processing. This effect was stronger for more analytically complex derivational affixes (
like + ly
) than more semantically transparent free base morphemes (
snow + man
). In contrast, children with dyslexia exhibited stronger activation during the free base condition relative to derivational affix condition. Taken together, the findings suggest that although children with dyslexia may struggle with derivational morphology, they may also use free base morphemes' semantic information to boost word recognition.

Conclusion:

This study informs literacy theories by identifying an interaction between reading ability, word structure, and how the developing brain learns to recognize words in speech and print.

Supplemental Material:

https://doi.org/10.23641/asha.25944949
DualAD: Dual adversarial network for image anomaly detection⋆
Authors: Wan Yonghao , Feng Aimin
Published: June 2024

Anomaly Detection, also known as outlier detection, is critical in domains such as network security, intrusion detection, and fraud detection. One popular approach to anomaly detection is using autoencoders, which are trained to reconstruct input by minimising reconstruction error with the neural network. However, these methods usually suffer from the trade‐off between normal reconstruction fidelity and abnormal reconstruction distinguishability, which damages the performance. The authors find that the above trade‐off can be better mitigated by imposing constraints on the latent space of images. To this end, the authors propose a new Dual Adversarial Network (DualAD) that consists of a Feature Constraint (FC) module and a reconstruction module. The method incorporates the FC module during the reconstruction training process to impose constraints on the latent space of images, thereby yielding feature representations more conducive to anomaly detection. Additionally, the authors employ dual adversarial learning to model the distribution of normal data. On the one hand, adversarial learning was implemented during the reconstruction process to obtain higher‐quality reconstruction samples, thereby preventing the effects of blurred image reconstructions on model performance. On the other hand, the authors utilise adversarial training of the FC module and the reconstruction module to achieve superior feature representation, making anomalies more distinguishable at the feature level. During the inference phase, the authors perform anomaly detection simultaneously in the pixel and latent spaces to identify abnormal patterns more comprehensively. Experiments on three data sets CIFAR10, MNIST, and FashionMNIST demonstrate the validity of the authors’ work. Results show that constraints on the latent space and adversarial learning can improve detection performance.
A deep model‐based channel interference mitigation for OTFS signals in ISAC systems
Authors: Zhou Wenkai , Wang Wenming , Qi Wanbin , Zhang Ronghui
Published: June 2024

In recent years, Orthogonal Time Frequency Space Modulation (OTFS) has gained popularity in integrated sensing and communications (ISAC) system due to its robustness against Doppler offset and delay changes. Traditional pilot‐based methods for accurate channel parameter estimation are complex and struggle with rapidly changing channel conditions. In this letter, a deep encode‐decode network (DED‐Net) is proposed. It uses DL to automatically learn and eliminate channel interference from OTFS signals. The framework employs a deep encoding and decoding network, similar to a filter, learning complex signal features to effectively remove interference. Our experiments demonstrate DED‐Net's ability to eliminate interference in OTFS modulation signals, offering an alternative to pilot‐based methods and showcasing DL's potential for ISAC systems.
Feature ensemble network for medical image segmentation with multi‐scale atrous transformer
Authors: Gai Di , Geng Yuhan , Huang Xia , Huang Zheng , Xiong Xin , Zhou Ruihua , Wang Qi
Published: June 2024

Recent years have witnessed notable advancements in medical image segmentation through deep convolutional neural networks. However, a notable limitation lies in the local operation of convolution, which hinders the ability to fully exploit global semantic information. To overcome the challenges prevalent in medical image segmentation, the feature ensemble network with multi‐scale atrous transformer is proposed. At the core of the approach lies the multi‐scale contextual integration module, which is based on the multi‐scale atrous transformer and facilitates contextual integration of multi‐level features. To extract discriminative fine‐grained features of the target region, a hybrid attention mechanism that synergistically combines spatial and channel attention, thereby sharpening the model's focus on crucial target information within high‐level features, is incorporated. Additionally, the channel‐aware feature reconstruction module is introduced as an innovative component engineered to tackle feature similarity issues across different categories. This module performs feature reconstruction based on channel perception, effectively widening the feature gap between categories and enhancing the segmentation capability. It is worth mentioning that our approach surpasses the state‐of‐the‐art method using three benchmark datasets in medical image segmentation.
Absolute velocity estimation of UAVs based on phase correlation and monocular vision in unknown GNSS‐denied environments
Authors: Deng Heng , Li Duhao , Shen Boyang , Zhao Zhiyao , Arif Usman
Published: June 2024

This paper proposes a novel approach for absolute velocity estimation of unmanned aerial vehicles in unknown and unmapped GNSS‐denied environments. The proposed method leverages the advantages of Fourier‐based image phase correlation and utilizes off‐the‐shelf onboard sensors, including a downward‐facing monocular camera, an inertial sensor, and a sonar. The non‐matching tracking approach is particularly appealing, offering accurate estimation while remaining robust against frequency‐dependent noise, significant intensity variations, and time‐varying illumination disturbances. In the proposed method, the first step involves computing global pixel motion from consecutive images using phase correlation, which utilizes the shift property of the Fourier transform. This pixel motion estimation serves as the basis for creating a closed‐loop solution for absolute velocity estimation. To further enhance accuracy, a Kalman filter is implemented to fuse all available data and provide a reliable velocity estimate. Validation of the proposed visual‐inertial technique is conducted through simulation experiments using AirSim and real‐world flight tests. The results demonstrate the practicality and effectiveness of the approach across a range of challenging scenarios.
Optimal control set expansion method for three‐phase voltage source converter with slip window selection and online parameter modification
Authors: Hu Linqiang , Lei Wanjun , Xiao Zhongxiu , Sun Xing
Published: June 2024

Finite control set model predictive control (FCS‐MPC) has been widely used in the control of three‐phase voltage source converter (VSC), but its control performance declines sharply at low sampling frequencies. This paper proposes an optimal control set expansion method with slip window selection and online parameter modification to improve the performance and robustness of FCS‐MPC. First, the performance of different control set expansion methods is compared. Then, an optimal control set expansion method that carries 12 virtual vectors is proposed. Last, a slip window selection strategy and an online parameter modification algorithm are proposed to reduce the computational burden and improve the controller's robustness respectively. Experimental result show that the proposed method significantly improves the control accuracy and carries good robustness and anti‐disturbance ability. Compared with existing typical MPC strategies, the proposed method obtains better current quality with lower switching frequency and requires less calculation. Moreover, the design of the proposed method is simple and there is no weighting factor to be tuned, which increases the method's practicality.
An optimal control method considering degradation and economy based on mutual learn salp swarm algorithm of an islanded zero‐carbon DC microgrid
Authors: Han Ying , Hou Yujing , Li Luoyi , Meng Weifeng , Li Qi , Chen Weirong
Published: June 2024

Due to the energy storage lifetime effects of the power allocation, there is a large space to improve the economy of the electric‐hydrogen hybrid DC microgrid. This paper provides an optimal control method based on the mutual learn salp swarm algorithm (MLSSA) in real‐time, which aims to enhance the economy and extend the system's service life. In order to realize the economic operation, operation cost and degradation cost of battery and hydrogen system are considered as the objective function first. Then, salp swarm algorithm based on mutual learn strategy is introduced to obtain optimal economy power allocation results in real‐time with higher convergence speed and increased accuracy. In addition, the proposed method also maintains the battery state of charge (SOC) and state of hydrogen charge (SOHC) within a proper range to guarantee the stable operation of the system. Finally, the results including power results, cost analysis and degradation rate analysis of the MATLAB/Simulink show that the proposed method is more economically beneficial than the non‐considering degradation cost strategy.
ALICE Highlights
Authors: Arsene Ionut Cristian
Published: June 2024

A summary including highlights of recent ALICE results, the status of the data taking in LHC Run 3 and of the ALICE detector upgrades is shown. The physics results are obtained mainly using the data recorded in pp, p–Pb and Pb–Pb during the LHC Run 2, but a set of results obtained in pp collisions with the Run 3 data is also discussed.
Collective dynamics - Theoretical overview
Authors: Kanakubo Yuuka
Published: June 2024

I overview the recent progress of phenomenological studies exploring collective dynamics in relativistic nuclear collisions to understand various QCD properties. Originally, collectivity was interpreted as a manifestation of the hydrodynamic behaviour of the QGP as a response to the initial collision geometry. Over the past decade, however, particularly following the experimental observation of momentum azimuthal anisotropy in small colliding systems, pioneering studies have demonstrated the possibility of other interpretations. In this talk, I highlight recent studies aimed at understanding various QCD properties at different collision stages through the lens of collectivity and emphasize the importance of establishing Monte Carlo event generators for relativistic nuclear collisions.
Theoretical developments on the initial state in relativistic particle collisions
Authors: Mäntysaari Heikki
Published: June 2024

We discuss recent progress towards developing accurate initial state descriptions for heavy ion collisions focusing on weak coupling based approaches, that enable one to constrain the high-energy structure of nuclei from deep inelastic scattering or proton-nucleus collisions. We review recent developments to determine the event-by-event fluctuating nuclear geometry, to describe gluon saturation phenomena at next-to-leading order accuracy, and to include longitudinal dynamics to the initial state descriptions.
Experimental overview on open-heavy flavours
Authors: Grosa Fabrizio
Published: June 2024

Heavy flavours, i.e. charm and beauty quarks, are recognised as excellent probes of the colour-deconfined medium created in ultrarelativistic heavy–ion collisions, the quark–gluon plasma (QGP). In this document, the most recent measurements of heavy-flavour hadrons in heavy-ion collisions are summarised. Measurements of nuclear modification factors and angular anisotropies of open charm and beauty hadrons are reported to investigate the properties of the heavy-quark interactions in the QGP. A particular focus is given to the measurement of heavy-flavour baryons and hadrons with strangequark content for the study of the hadronisation mechanisms in both heavy–ion and proton–proton collisions. Finally, the polarisation of the charm vector mesons D*+ and J/ψ in heavy–ion collisions is presented.
Experimental overview: Quarkonium production in relativistic particle collisions
Authors: Ståhl Leiton Andre Govinda
Published: June 2024

Quarkonium provides a golden probe of the formation of the Quark Gluon Plasma (QGP). Its production in heavy-ion collisions can be affected by an interplay of different phenomena such as medium-induced dissociation and heavy-quark (re)combination in the QGP, and cold nuclear matter effects due to the presence of the nucleus. The measurement of quarkonium states in different collision systems and beam energies allows to gain further insights on these effects and probe the properties of the QGP. In these proceedings, an overview of the main experimental quarkonium measurements in proton-proton, protonnucleus and nucleus-nucleus collisions is presented, giving emphasis on the most recent results from the Large Hadron Collider and Relativistic Heavy Ion Collider experiments shown at the 2023 Quark Matter conference.
Open heavy flavor and quarkonium - Theoretical overview
Authors: Escobedo Espinosa Miguel Ángel
Published: June 2024

This is a theoretical overview of the new results regarding open heavy flavor and quarkonium presented in the Quark Matter 2023 conference.
Emergent conformality in high-density matter
Authors: Sasaki Chihiro , Marczenko Michał , McLerran Larry , Redlich Krzysztof
Published: June 2024

This contribution discusses baryon-rich matter in the context of recent astrophysical observations of neutron stars (NSs) and the structure of the equation of state that arises from these observations. We show that, generating an ensemble of equations of state that fulfill multimessenger constraints, the speed of sound and trace anomaly can reach their conformal values at the center of maximally massive NSs. The local peak of the speed of sound appears at values of the energy and particle densities being consistent with deconfinement and percolation conditions of QCD phase transition. Some implications of advocating a pseudo-conformal symmetry in dense matter are presented.
Bayesian calibration of viscous anisotropic hydrodynamic (VAH) simulations of heavy-ion collisions
Authors: Heinz Ulrich , Liyanage Dananjaya , Gantenberg Cullen
Published: June 2024

A Bayesian calibration, using experimental data from 2.76 A TeV Pb-Pb collisions at the LHC, of a novel hybrid model is presented in which the usual pre-hydrodynamic and viscous relativistic fluid dynamic (vRFD) stages are replaced by a viscous anisotropic hydrodynamic (VAH) core that smoothly interpolates between the initial expansion-dominated, approximately boost-invariant longitudinally free-streaming and the subsequent collision-dominated (3+1)- dimensional standard vRFD stages. This model yields meaningful constraints for the temperature-dependent specific shear and bulk viscosities, (η=s)(T) and (ζ=s)(T), for temperatures up to about 700MeV (i.e. over twice the range that could be explored with earlier models). With its best-fit model parameters the calibrated VAH model makes highly successful predictions for additional pT-dependent observables for which high-quality experimental data are available that were not used for the model calibration.
Tracing baryon and electric charge transport in isobar collisions
Authors: Pihan Gregoire , Monnai Akihiko , Schenke Björn , Shen Chun
Published: June 2024

It is of fundamental interest to understand the carrier of conserved quantum charges within protons and nuclei at high energy. Preliminary data from isobar collisions at RHIC reveal a scaled net-baryon to net-electric charge ratio (B/ΔQ × ΔZ/A) at midrapidity between 1.2 and 2, consistent with string junction model predictions. Here, we compute the initial stage scaled net-baryon to net-electric charge ratio for isobar collisions. Our model incorporates a realization of the string junction model and models the nuclear structure. Our predictions identify the baseline expectations for such measurement and quantify the impact of the nuclear structure.
Thermal dilepton production in heavy-ion collisions at beam-energy-scan (BES) energies
Authors: Churchill Jessica , Du Lipei , Forster Bailey , Gao Han , Jackson Greg , Jeon Sangyong , Gale Charles
Published: June 2024

The lepton pair production rate at finite temperature and at nextto-leading-order (NLO) is calculated for quark-gluon plasma at non-zero net baryon density. Yields are obtained using a (3+1)D multicomponent simulation capable of reproducing hadronic observables measured in the RHIC Beam Energy Scan (BES). Spectra of intermediate invariant mass dileptons are compared with measurements from the STAR collaboration. The result of a study where the temperature information obtained from the dilepton spectrum from heavyion collisions performed at different energies and centralities is presented.
Measurements of charm quark interaction with the QGP in heavy-ion collisions at STAR
Authors: Su Yuan
Published: June 2024

We report the measurements of D0 transverse momentum (pT) differential invariant spectra with pT < 8 GeV/c for 0–10%, 10–40%, and 40–80% centrality classes in isobar collisions (96Ru + 96Ru and 96Zr + 96Zr) at √SNN = 200 GeV with the STAR experiment. The strong suppression of D0 yield is observed for pT > 3 GeV/c in the central collisions, demonstrating that charm quarks suffer significant energy loss in the bulk QCD medium. In this proceeding, the measurements of D0 meson tagged jets at √SNN = 200 GeV in Au+Au collisions are reported as well. We present the nuclear modification factor, RCP, as a function of pT,Jet and zJet, and show the ratios of the D0 radial profile with 1<pT,D0 < 10 GeV/c. Comparisons to model calculations for D0 and D0-tagged jets are also discussed.
Measurements of charm quark production and hadronization at CMS
Authors: Chandra Soumik
Published: June 2024

The study of charm quark hadrons is an important probe into the hadronization of heavy quarks. More specifically, we present results on the production of Λc baryon, the nuclear modification factors (RAA), and the Λc/D0 yield ratios at √SNN = 5.02 TeV in proton-proton (pp) collisions and in different centrality regions in lead-lead (PbPb) collisions, using data recorded with the CMS detector in 2017 and 2018, respectively. The reported RAA for Λc provides useful information regarding the energy loss mechanism and the hadronization processes of charm quark in the quark-gluon plasma. The transverse momentum (pT) dependence of the RAA is similar to that of other charm and beauty hadrons but with its minimum shifted towards higher pT. Comparing the Λc/D0 production ratio in pp and PbPb collisions suggests that coalescence as a hadronization process is not significant for pT > 10 GeV/c. The ratio becomes comparable to the measurements in e+e− collisions for pT > 30 GeV/c. We also present results of the Λc baryon and D0 meson production and their ratios in proton-lead (pPb) collisions at √SNN = 8.16 TeV as a function of pT and final-state multiplicity using the data recorded by the CMS experiment in 2016. We do not observe significant multiplicity dependence for the baryon over meson ratio for charm hadrons. Based on a previous study, the difference between the results from charm quarks and those from light quarks suggests coalescence processes for heavy quarks do not increase further with multiplicity, unlike light quarks.
Studies of the relative suppression of excited quarkonium states with CMS
Authors: Kukral Ota
Published: June 2024

One of the findings of the LHC heavy ion program is the observation of stronger suppression of the excited quarkonium states compared to the ground states in lead-lead (PbPb) and proton-lead (pPb) collisions. Such differences among the states may imply dissociation effects occurring at late stages, after the evolution of heavy quark pairs into well-defined physical states. The variety of binding energies within the quarkonia families offers an experimental tool to characterize the phenomena at play. Measuring the excited states is crucial as they represent significant feed-down contributions to the production of the ground states and must be accounted for in the interpretation of the data. We present studies of the relative suppression of quarkonia in pPb and PbPb collisions performed by CMS. For the first time, the nuclear modification factor has been extended to the strongly suppressed Υ(3S) state. Moreover, nuclear modification factors as well as excited-to-ground state cross section ratios are measured as a function of particle transverse momentum and rapidity, and event activity. The results are compared with several model calculations incorporating initial- and final-state effects.
Heavy flavor and quarkonia results from the PHENIX experiment
Authors: Smith Krista
Published: June 2024

The PHENIX experiment at RHIC has a unique large rapidity coverage (1.2 < |y| < 2.2) for heavy flavor studies in heavy-ion collisions. This kinematic region has a smaller particle density and may undergo different nuclear effects before and after the hard process when compared to mid-rapidity production. The latest PHENIX runs contain a large data set which allows, for the first time, the study of heavy flavor and J/ψ flow at the large rapidity region in Au+Au collisions at √SNN =200 GeV. This measurement has the potential to reveal a medium evolution distinct from that known at mid-rapidity.
Search for medium response to jet quenching via hadron chemistry around the quenched jets
Authors: Luo Ao , Mao Ya-Xian , Qin Guang-You , Wang En-Ke , Zhang Han-Zhong
Published: June 2024

We present a unique signal of medium response to jet quenching: the enhancement of baryon-to-meson ratios for associated particles at intermediate transverse momentum around the triggered quenched jets. Since the lost energy from the jets can diffuse to large angles, we expect that such baryon-to-meson enhancement is stronger for larger distance with respect to the jet axis. Our prediction can be tested by future experiments.
Isolating final state effects in high pT π0 production using direct photons in small system collisions with PHENIX
Authors: Firak Daniel
Published: June 2024

PHENIX observed a 20% suppression in the production of high pT neutral pions in the most central (0-5%) d+Au collisions at 200 GeV. Through the simultaneous measurement of high pT direct photons (γdir) and π0 production for event samples selected by event activity, the final state effects could be disentangled from cold-nuclear-matter effects and event-selection biases that are inherent in using the standard Glauber model. This isolation of final state effects is achieved by approximating the nuclear modification factor by the double ratio RxA = (γdir/π0)pp/(γdir/π0)xA. While the cold-nuclear-matter effects in x+A collisions cancel in the (γdir/π0) ratio, the effective number of binary collisions is given by NCollEXP = γxAudir / γppdir, which eliminates the dependence on the Glauber model. In addition, many systematic uncertainties cancel in the double ratio.
Exploring the hadron gas phase of relativistic heavy-ion collisions at the LHC
Authors: Agrawal Neelima
Published: June 2024

The lifetimes of hadronic resonances are comparable to the one of the hadron gas phase created in the late stages of high-energy nuclear collisions. Therefore, a significant fraction of resonances decay inside a high-density medium and their decay daughters rescatter with nearby hadrons destroying their initial kinematic correlations. The competing effect of resonance regeneration via pseudo-elastic interactions of hadrons interplays between the rescattering effect, modifying the measured yields and transverse-momentum spectra of hadronic resonances. These effects are studied by measuring the production yield ratio of resonances to the corresponding long-lived particle as a function of the hadronic lifetime, i.e. charged-particle multiplicity. In addition, measurements of the differential yields of resonances with different masses, quark content, and quantum numbers help in understanding particle production mechanisms, strangeness production, and parton energy loss. This article reports recent ALICE measurements on resonances and the use of the experimental input in a partial chemical equilibrium-based thermal model to constrain the kinetic freezeout temperature. This is a novel procedure that is independent of assumptions on the flow velocity profile and the freeze-out hypersurface.
Testing eigenstate thermalization hypothesis for non-Abelian gauge theories
Authors: Yao Xiaojun , Ebner Lukas , Müller Berndt , Schäfer Andreas , Seidl Clemens
Published: June 2024

We report on progress in full quantum understanding of thermalization in non-Abelian gauge theories. Specifically, we test the eigenstate thermalization hypothesis for (2+1)-dimensional SU(2) lattice gauge theory.
Wee partons in QCD and gravity: Double copy and universality
Authors: Raj Himanshu , Venugopalan Raju
Published: June 2024

We discuss a quantitative “double copy” between radiation from shockwave collisions in Einstein gravity and in QCD. The correspondence extends to 2 → N amplitudes in Regge asymptotics. The classicalization and unitarization of these amplitudes at maximal occupancy, corresponding to black hole and Color Glass Condensate (CGC) states respectively, are described by the emergent Goldstone dynamics of wee partons. We outline some consequences of the universal dynamics on both sides of the correspondence.
Finite volume effects near the chiral crossover
Authors: Kara Ruben , Borsányi Szabolcs , Fodor Zoltán , Guenther Jana N. , Parotto Paolo , Pásztor Attila , Wong C. H.
Published: June 2024

The effect of a finite volume presents itself both in heavy ion experiments as well as in recent model calculations. The magnitude is sensitive to the proximity of a nearby critical point. We calculate the finite volume effects at finite temperature in continuum QCD using lattice simulations. We focus on the vicinity of the chiral crossover. We investigate the impact of finite volumes at zero and small chemical potentials on the QCD transition though the chiral observables.
High order fluctuations of conserved charges in the continuum limit
Authors: Borsányi Szabolcs , Fodor Zoltán , Guenther Jana N. , Katz Sändor D. , Parotto Paolo , Pásztor Attila , Pesznyák Dávid , Szabó Kálmán K. , Wong Chik Him
Published: June 2024

We calculate the grand canonical fluctuations of the net baryon number and net strangeness in the region of chiral crossover from lattice QCD. We continuum extrapolate the results in a finite volume using the new 4HEX discretization scheme up to χ6B and also for χ8B at a single temperature. Our results contradict recent estimates at a finite lattice spacing. See Ref. [1] for the full version of this work.
Two-particle femtoscopy at the HADES experiment
Authors: Grunwald Mateusz
Published: June 2024

An experimental study of γ-γ, p-Λ, and p-cluster femtoscopic correlations from Ag+Ag collisions at √SNN = 2.55 GeV has been conducted using the HADES detector. The results reveal an HBT-like structure for γ-γ correlations, provide estimations of strong interaction parameters for p-Λ correlations, and indicate potential decay signatures of light nuclei for p-cluster correlation functions.
Lattice-based equation of state with 3D ising critical point
Authors: Kahangirwe Micheal , Bass Steffen A. , Jahan Johannes , Moreau Pierre , Parotto Paolo , Ratti Claudia , Soloveva Olga , Stephanov Misha , Bratkovskaya Elena
Published: June 2024

The BEST Collaboration equation of state combining lattice data with the 3D Ising critical point encounters limitations due to the truncated Taylor expansion up to μB/T ~ 2.5. This truncation consequently restricts its applicability at high densities. Through a resummation scheme, the lattice results have been extended to μB/T = 3.5. In this article, we amalgamate these ideas with the 3D-Ising model, yielding a family of equations of state valid up to μB = 700MeV with the correct critical behavior. Our equations of state feature tunable parameters, providing a stable and causal framework-a crucial tool for hydrodynamics simulations.
Measuring jet quenching with a Bayesian inference analysis of hadron and jet data by JETSCAPE
Authors: Ehlers Raymond
Published: June 2024

The JETSCAPE Collaboration reports the first multi-messenger study of the QGP jet transport parameter q^ using Bayesian inference, incorporating all available hadron and jet inclusive yield and jet substructure data from RHIC and the LHC. The theoretical model utilizes virtuality-dependent in-medium partonic energy loss coupled to a detailed dynamical model of QGP evolution. Tension is observed when constraining q^ for different kinematic cuts of the inclusive hadron data. The addition of substructure data is shown to improve the constraint on q^, without inducing tension with the constraint due to inclusive observables. These studies provide new insight into the mechanisms of jet interactions in matter, and point to next steps in the field for comprehensive understanding of jet quenching as a probe of the QGP.
Looking for QGP signatures in ultraperipheral PbPb collisions
Authors: Das Sruthy Jyothi
Published: June 2024

Ultraperipheral collisions of relativistic heavy ion beams lead to a diverse set of photon-nucleus (photonuclear) interactions. The measurements of particles and their interaction produced in photonuclear reactions can shed light on the QCD dynamics of these novel, extremely asymmetric colliding systems, with energies between those available at RHIC and the LHC. Previous studies by ATLAS indicate significant elliptic and triangular flow coefficients in these events [1]. Thus, it is imperative to check these events for other potential QGP signatures including radial flow, strangeness enhancement, and enhanced baryon/meson production. This proceeding presents the measurement of charged hadron yields in photonuclear collisions using 5.02 TeV Pb+Pb data collected in 2018 by ATLAS, with a dedicated photo-nuclear event trigger. The charged hadron yields are presented as a function of pseudorapidity and transverse momentum in different categories of event multiplicity. The results are compared with 5.02 TeV p+Pb data collected in 2016 by ATLAS, at the same event multiplicities. The results are also compared with calculations from DPMJET and hydrodynamic-based models. These comparisons enable detailed characterizations of photonuclear collision properties, including the photon energy distribution, and whether small QGP droplets may be formed.
Human Behaviour Analysis Using CNN
Authors: Budhewar Anupama , Purbuj Sanika , Rathod Darshika , Tukan Mrunal , Kulshrestha Palak
Published: June 2024

Emotion recognition has been the subject of extensive research due to its significant impact on various domains, including healthcare, human-computer interaction, and marketing. Traditional methods of emotion recognition rely on visual cues, such as facial expressions, to decipher emotional states. However, these methods often fall short when dealing with individuals who have limited ability to express emotions through facial expressions, such as individuals with certain neurological disorders.

This research paper proposes a novel approach to emotion recognition by combining facial expression analysis with electroencephalography (EEG) data. Deep learning techniques are applied to extract features from facial expressions captured through video analysis, while simultaneously analyzing the corresponding EEG signals. The goal is to improve emotion recognition accuracy by utilizing the complementary information offered by the interaction between facial expressions and EEG data.

Emotion recognition is a challenging task that has collected considerable recognition in the current years. Different and refined approaches to recognize emotions based on facial expressions, voice analysis, physiological signals, and behavioral patterns have been developed. While facial expression analysis has been a dominant approach, it falls short in instances where individuals cannot effectively express emotions through their faces. To overcome these limitations, there is a need to explore alternative methods that can provide a more accurate assessment of emotions. This research paper aims to investigate the collaboration and interaction between facial expressions and EEG data for emotion recognition. By combining the information from both modalities, it is expected to augment the accuracy and strength of emotion recognition systems. The proposed method can range from conducting literature reviews to designing and fine-tuning deep learning models for feature extraction, developing fusion models to combine features from facial expressions and EEG data, performing experimentation and evaluation, writing papers and documentation, preparing presentations for dissemination, and engaging in regular meetings and discussions for effective collaboration. Ethical considerations, robustness and generalizability, continual learning and skill development, and utilizing collaboration tools and platforms are also essential contributions to ensure the project’s success.
Lung Cancer Prognosis Through Standardized Pre-Processing And Multifaceted Data Enhancement: A Deep Learning Approach
Authors: Nathan Revathy , Rithani M.
Published: June 2024

With high rates of morbidity and mortality, lung cancer continues to be a major problem for world health. A precise prognosis is essential for clinical judgment and patient care. This study provides a unique deep learning method for enhancing the prognosis of lung cancer through multimodal data enhancement and standardized pre-processing. The proposed methodology begins with the comprehensive pre-processing of diverse patient data sources, including medical images, clinical records, and genomic information. Standardization techniques are applied to ensure data consistency and reliability, reducing noise and enhancing the quality of the input data. Furthermore, feature selection and extraction methods are employed to identify the most informative variables for prognostic prediction. To harness the full potential of the integrated data, a deep learning architecture is developed. This architecture combines convolutional neural networks (CNNs) for image analysis, recurrent neural networks (RNNs) for sequential clinical data, and fully connected layers for genomic information. By fusing these diverse data modalities, the model captures intricate patterns and relationships, enabling more accurate prognosis. This research paper introduces a cutting-edge deep learning approach for lung cancer prognosis that leverages standardized pre-processing and multifaceted data enhancement. By integrating medical images, clinical records, and genomic information, our model provides clinicians with a powerful tool for improving patient outcomes through more precise prognostic predictions. This research contributes to the advancement of personalized medicine in lung cancer management, offering new avenues for early intervention and tailored treatment strategies.
Transforming Domain Data into Semantic Models for Content Delivery Applications
Authors: Tempelfeld Lars , Wiedewilt Celine , Kruse Anna-Maria , Ziegler Wolfgang
Published: June 2024

This research focuses on transforming metadata from different systems in energy technology for transportation systems to create an ontology to dynamically provide cross-system information. Thereby, different approaches for transforming domain data and domain knowledge into ontology are investigated. We therefore analysed, which approach is best suited to the given business situation. In addition, the study and discussion address various challenges related to metadata extraction and transformation. Finally, this paper emphasizes the central role of ontologies and SCR for automation in the technical documentation and highlights the potential resulting from this integration strategy.
Update on the Use of Thrombopoietin-Receptor Agonists in Pediatrics
Authors: Gebetsberger Jennifer , Streif Werner , Dame Christof
Published: June 2024

This review summarizes the rationale and current data on the use of thrombopoietin receptor agonists (TPO-RAs) for treating severe thrombocytopenia in infants, children, and adolescents. It focuses on substances that have been approved by the U.S. Food and Drug Administration (FDA) and European Medicines Agency (EMA) for pediatric patients. Romiplostim and eltrombopag are already established as second-line treatment for persistent or chronic immune thrombocytopenia (ITP). As in adults, TPO-RAs are currently also evaluated in severe aplastic anemia (SAA), chemotherapy-induced thrombocytopenia (CIT), myelodysplastic syndromes (MDS), and poor engraftment after hematopoietic stem cell transplantation in pediatric and adolescent patients. Moreover, studies on the implication of TPO-RA in treating rare inherited thrombocytopenias, such as Wiskott-Aldrich syndrome (WAS), congenital amegakaryocytic thrombocytopenia (CAMT), or MYH9-associated thrombocytopenia, deserve future attention. Current developments include testing of avatrombopag and lusutrombopag that are approved for the treatment of thrombocytopenia associated with chronic liver disease (CLD) in adult patients. In pediatric and adolescent medicine, we expect in the near future a broader use of TPO-RAs as first-line treatment in primary ITP, thereby considering immunomodulatory effects that increase the rate of sustained remission off-treatment, and a selective use in rare inherited thrombocytopenias based on current clinical trials.
Editorial
Authors: Aringer Martin
Published: June 2024

Sehr geehrte Damen und Herren, liebe Kolleginnen und Kollegen,

Kollagenosen behandeln heißt zunächst die Menschen zu betreuen und begleiten, die an
diesen oft unberechenbaren chronischen Erkrankungen leiden. Dazu kommt
Fingerspitzengefühl für ein breites Spektrum der Therapie von Abwarten oder ganz
milden Maßnahmen bis zur vollen Stärke der Biologika und der Immunsuppression.
Wirklich alle Organsysteme können betroffen sein, und das fordert nicht nur breite
internistische Kernkompetenz, sondern macht auch interdisziplinäre Zusammenarbeit
notwendig.
Entbürokratisierung
Authors: Schulte Barbara
Published: June 2024

Die Begriffe „Entbürokratisierung“ und „Bürokratieabbau“ sind seit einiger Zeit im
Gesundheitswesen in aller Munde. Jedoch handelt es sich bisher lediglich um leere
Hülsen. Im Gegenteil – es kommen stetig neue Gesetze hinzu. Viele davon führen sogar
noch zu einem Bürokratieaufbau. Auch die versprochene Krankenhausreform, bzw. das
Krankenhausversorgungsverbesserungsgesetz, führen sowohl in der Planungs- und der
Vergabephase als auch in der prospektiven Überwachung zu erheblichen zusätzlichen
bürokratischen Aufwänden, so die Einschätzung vieler Praktiker. Schon jetzt ist uns
mit den Pflegebudgets, den Mindestmengen, den Personaluntergrenzen etc. ein enges
Korsett gesetzt. Die Sinnhaftigkeit wird von vielen Seiten infrage gestellt. Das
System Krankenhaus wird mit jedem Jahr schnelllebiger und komplizierter. Viele
Stränge und Knackpunkte des Systems müssen zusammengeführt werden. Aufgrund dieser
hohen Komplexität, die sowohl im ambulanten als auch im stationären Sektor zu finden
ist, dürfen keine unnötigen zusätzlichen Arbeitsbelastungen entstehen. Dazu zählt
auch das notwendiges Mindestmaß an Dokumentations- und Nachweisverpflichtungen nicht
überzustrapazieren.
Datennutzende Medizin, Datengetriebene Diabetestechnologie
Authors: Thomas Andreas , Sahm Claudia
Published: June 2024

Per Definition sind Daten gewonnene Zahlenwerte auf Grundlage von Beobachtungen, Messungen, statistischen Erhebungen, Auswertungen usw. Sie sind der Versuch, die sich dabei ergebenden Fakten in nachvollziehbare, durch Zahlenwerte ausdrückbare Größen umzuwandeln. Damit lassen sich technische Geräte, aber auch der Gesundheitszustand von Menschen beschreiben, letzteres sicher bei weitem nicht vollständig. Im medizinischen Kontext verifizieren Daten also Beobachtungen und Erfahrungen des medizinischen Personals in Bezug auf die Behandlung der Patienten. Gleichfalls betrifft dies auch die Patienten selbst bezüglich ihrer eigenen Befindlichkeit, beschreiben die Daten doch den Gesundheitsstatus und den Erfolg einer bestehenden Therapie. Die steigende Vielfalt von Parametern aus Messungen, zum Therapieverlauf sowie zusätzliche Eingaben der Patienten erfordern aber eine schnelle Analyse und übersichtliche Darstellung, wofür eine gut handhabbare Software notwendig ist. Dieser Zugang zur Digitalisierung erweitert die Möglichkeiten für die Entwicklung von Therapiemanagementsystemen, Clinical Decision Support Systemen und Patient Decision Support Systemen. Im folgenden Überblick wird darauf eingegangen.
Systemische Sklerose
Authors: Biedermann Sophie , Riemekasten Gabriela
Published: June 2024

Die Systemische Sklerose (Synonym: systemische Sklerodermie) ist eine nicht
heilbare Autoimmunerkrankung mit einem hohen Einfluss auf die Lebensqualität und
die Morbidität der Patienten. Sie zählt zu den Kollagenosen und ist
gekennzeichnet durch eine generalisierte Mikroangiopathie, die Produktion von
Autoantikörpern und durch Dysfunktion der Fibroblasten, die zu erhöhter
Produktion von extrazellulärer Matrix führt. Neben der Hautbeteiligung mit
akralen Ulzerationen und Autoamputationen kann eine Beteiligung der inneren
Organe wie Lunge, Herz, Gastrointestinaltrakt und Niere vorkommen. Die
Haupttodesursachen stellen die interstitielle Lungenerkrankung, die
pulmonal-arterielle Hypertonie und die kardiale Manifestation dar. Es besteht
eine Assoziation mit bestimmten antinukleären Antikörpern, die Pathogenese ist
bis heute noch weitestgehend unverstanden. Es gibt derzeit erste Ansätze,
Krankheitsmechanismen und die Beziehung zwischen der obliterativen Vaskulopathie
und der Fibrose zu verstehen. Die Therapie sollte so frühzeitig wie möglich im
Krankheitsverlauf beginnen und ist je nach Organbefall eine Kombination aus
Immunsuppressiva, antifibrotischer oder vasodilatativer Therapie.
Süßstoffe und ihre Wirkung auf das Körpergewicht
Authors: Kabisch Stefan
Published: June 2024

Süßstoffe sind chemisch, geschmacklich und biologisch heterogene, hochpotente Süßungsmittel, die in kalorienreduzierten Getränken, hochprozessierten Speisen und diätetischen Ersatzmahlzeiten verwendet werden. Maximal empfohlene Verzehrsmengen (“acceptable daily intake”, ADI) werden bei ausgewogener Ernährung und bei ausgeprägter Nutzung trotz konservativer Auslegung meist nicht erreicht. Süßstoffmischungen begünstigen die Unterschreitung der ADIs und die Geschmacksoptimierung. Epidemiologisch ist Süßstoffverzehr mit Adipositas, Typ-2-Diabetes, kardiovaskuläre Erkrankungen und Krebs assoziiert. Starke Confounder (andere Lebensstilfaktoren) und „reverse causality“ schwächen aber die Aussagekraft.

In verblindeten randomisiert-kontrollierten Studien (RCTs) bewirken Süßstoffe gegenüber Zucker eine signifikante Reduktion von Körpergewicht und Leberfett, während glykämische Parameter zum Nachteil trenden. Der Gewichtsverlust liegt unter der Erwartung gemäß der theoretisch eingesparten Kalorien. Psychologische oder metabolische Störeffekte der Süßstoffe sind daher aktuell Gegenstand intensiver Forschung, bei der das Inkretinsystem, die Konditionierung auf Süßreize und das Darmmikrobiom Ansatzpunkte darstellen.
Uroperitoneum bei einem weiblichen Jungrind
Authors: Lausch Corinna K. , Lorch Annette
Published: June 2024

Bei weiblichen Jungrindern ist ein Uroperitoneum ein selten dokumentierter
Befund. Häufiger ist es bei männlichen Jungrindern als Folge einer obstruktiven
Urolithiasis oder einer Urachitis bzw. eines Urachusabszesses beschrieben. In
diesem Bericht wird der Fall eines Uroperitoneums infolge einer vermutlich
traumatisch bedingten Harnblasenruptur bei einem 8,5 Monate alten weiblichen
Jungrind geschildert. Das Tier zeigte zum Zeitpunkt der Einlieferung in die
Klinik ein hochgradig vermehrt gefülltes Abdomen bei erstaunlicherweise
vorhandenem Harnabsatz. Bei der Palpation des Abdomens konnte eine deutliche
Undulationswelle ausgelöst werden. Es lag zudem eine begleitende
Labmagenverlagerung nach links vor. Auf eine ultrasonografische Untersuchung und
eine Bauchhöhlenpunktion folgte eine diagnostische Laparotomie. Der Harn wurde
fraktioniert aus der Bauchhöhle abgelassen, der geschädigte Harnblasenpol
abgesetzt und die Harnblase verschlossen. Der Labmagen wurde reponiert und
omentopexiert. Das Jungrind konnte geheilt entlassen werden und befand sich 5
Jahre später noch in der Herde.