bluuebunny/crossref_metadata_2025 huggingface dataset:
Quantum Entanglement and Non-Hermiticity in Free-Fermion Systems
Authors: Chen Li-Mei , Zhou Yao , Chen Shuai A. , Ye Peng
Published: November 2024

This topical review article reports rapid progress on the generalization and application of entanglement in non-Hermitian free-fermion quantum systems. We begin by examining the realization of non-Hermitian quantum systems through the Lindblad master equation, alongside a review of typical non-Hermitian free-fermion systems that exhibit unique features. A pedagogical discussion is provided on the relationship between entanglement quantities and the correlation matrix in Hermitian systems. Building on this foundation, we focus on how entanglement concepts are extended to non-Hermitian systems from their Hermitian free-fermion counterparts, with a review of the general properties that emerge. Finally, we highlight various concrete studies, demonstrating that entanglement entropy remains a powerful diagnostic tool for characterizing non-Hermitian physics. The entanglement spectrum also reflects the topological characteristics of non-Hermitian topological systems, while unique non-Hermitian entanglement behaviors are also discussed. The review is concluded with several future directions. Through this review, we hope to provide a useful guide for researchers who are interested in entanglement in non-Hermitian quantum systems.
Suppression of clock-jitter noise and laser phase noise in arm locking
Authors: Wu Zhang-Qi , Wang Pan-Pan , Ke Jun , Shao Cheng-Gang
Published: November 2024

Arm-locking technique has been a focus of attention as one of the means to suppress the laser phase noise in space-based gravitational wave detector. The main idea of the arm-locking technique is to transfer the stability of the detector arm length to laser frequency by introducing a feedback control loop. Generally, laser phase noise will be suppressed by an amount similar to the magnitude of the controller gain. However, on the one hand, clock-jitter noise and optical bench motion noise, as the noise floor of the arm-locking technique, need to be suppressed. On the other hand, limited by the Doppler frequency pulling, the gain of the controller generally cannot be too large. It means that even if we do not consider clock-jitter noise and optical bench motion noise, it is difficult to suppress laser phase noise below the noise floor only by arm-locking technique. In this work, we combine self-referenced optical frequency combs and arm-locking technique to generate clock signals that are coherently referenced to the closed-loop laser beams, so that the clock-jitter noise is also suppressed by about the level of controller gain. We conduct a simulation on the above configuration, and the results show that the performance of the arm-locking is no longer limited by clock-jitter noise in the low-frequency band. To address the issue of insufficient laser phase noise suppression by the arm-locking technique, we further investigate time-delay interferometry (TDI) combinations under outputs of arbitrary arm-locking configurations. We obtain the equations for eliminating laser phase noise. To ensure that the TDI combinations can directly operate in the time domain, we derive a restricted solution space by assuming a specific form for the solutions.
Dynamic modelling and mechanical analysis of an inertial linear piezoelectric actuator with double stators
Authors: Xiong Yue , Yang Lin , Hong Xinwei , Wen Jiaquan , Zhang Jie
Published: November 2024

To achieve higher output performance in precision applications, and in order to gain deeper insight into operation principle, a dynamic model is established including the transmission relationship between the stators, the driving shaft, and the mover based on a slip–slip type inertial linear piezoelectric actuator with double stators. The influence of stator material and symmetry ratio of the excitation signal on the vibration characteristics of the stator and the output performance are investigated. This study reveals the relationship between these factors through a combination of simulations and experiments. Prototypes using several materials are machined and assembled, and the vibration characteristics and output performance are rigorously tested. The results obtained from the experimental measurements are consistent with the simulation results, confirming the validity and rationality of the dynamic model. The experimental results of the stator, as well as the actuator, are analyzed. It can be concluded that the actuator is able to output a larger effective displacement in one motion period with the increased symmetry ratio of the triangular wave signal, applied with a higher operating frequency, resulting in a higher output velocity of 42 mm s−1 and a larger load of 300 g with the steel stator, and a higher displacement resolution of 18 nm can be achieved using the AL alloy stator. This research not only provides an effective method for enhancing and selecting the desired output performance for the researched actuator but also can be extended to other slip-slip type inertial linear piezoelectric actuators.
Estimation, tuning, and evaluation of propagation direction behavior in superimposed orthogonal two-dimensional structure-borne traveling waves
Authors: Rogers William C , Albakri Mohammad I
Published: November 2024

Structure-borne traveling waves (SBTW) are observed in nature as a means for propulsion and locomotion of creatures on land and in the ocean. Recently, various approaches have been investigated to replicate this phenomenon. Previous studies have successfully generated SBTWs suitable for propulsion applications and particle motion on active surfaces. Much recent literature has focused on generating traveling waves that propagate only along a single axis for 1D and 2D structures. This limits their potential and does not take advantage of the full potential of 2D structures. This study examines the potential of employing superposition to control the propagation direction of 2D SBTW. This is investigated numerically using an experimentally validated Finite Element model of a 2D plate with piezoelectric actuators. The individual SBTWs are superimposed by simultaneously exciting two pairs of actuators that are aligned orthogonally on the surface of a plate. Traveling waves are excited in the plate using two-mode excitation. Structural intensity is utilized to develop quantifiable metrics to describe the overall propagation direction and uniformity, which are necessary for describing the complex propagation patterns encountered with 2D SBTW. The potential of the proposed approach along with developed tuning and evaluation methods are demonstrated through case studies of two plates, one square and one rectangular. For both cases, the overall direction of the SBTW is tuned to propagate for any direction between the individual SBTW. This was achieved while maintaining a high-quality overall SBTW. With this approach, 2D SBTW can be steered for wave-driven motion applications such as propulsion of the structure itself or conveying particles in any direction along the structure’s surface without compromising the quality of the overall traveling wave.
Evolution of critical current density in CaKFe4As4 with La-doping
Authors: Chen Yangsong , Wang Chunlei , Zu Yuhang , Kobayashi Yuto , Ichinose Ataru , Sakagami Ryosuke , Tamegai Tsuyoshi
Published: November 2024

Single crystals of (Ca1−x
La
x
)KFe4As4 (0

≤

x

≤

0.16) have been grown by using the self-flux method, and the evolution of physical properties including the critical current density (J
c

)

with La-doping has been investigated. T
c decreases monotonically with increasing x, while J
c at the same temperature and magnetic field increases initially and reach its maximum at x

=

0.082

. The increase in J
c is more obvious at low temperatures and high fields. At T = 5 K and H = 40 kOe, J
c reaches 0.34 MA cm−2, which is

∼
4

times larger than that for pure crystals. It is also found that anomalous temperature dependence of J
c in CaKFe4As4 is wiped away as the La content is increased. However, J
c shows non-monotonic field dependence (peak effect) at high fields in crystals with large x. In addition, we found that despite weak anisotropy of H
c2, there is extremely large anisotropy of J
c up to

∼
15

, which is most likely caused by novel planar defects in the crystal, similar to CaKFe4As4. J
c characteristics in (Ca1−x
La
x
)KFe4As4 with disorder outside FeAs planes is compared with that in CaK(Fe1−x
Co
x
)4As4 with disorder within FeAs planes.
The effect of single walled carbon nanotubes on conductivity and thermal properties of glass fiber reinforced composites
Authors: Bayirli Mehmet , Ilgaz Aykut
Published: November 2024

This paper reports on a comprehensive study of the effect of single-walled carbon nanotube (SWCNT) on the alternating current (AC) conductivity, thermal and morphological properties of the unsaturated polyester based glass fiber reinforced polymer composite (GFRPC). AC conductivity measurements were carried out using the impedance spectrum and thermal measurements were carried out using differential scanning calorimetry (DSC) at a temperature range of 24 °C–900 °C and heating rates of 20 °C min−1. Impedance results showed that the conductivity behavior in the nanotube-loaded composite laminate obeys a Jonscher-type mechanism. At low frequencies, the conductivity value remains almost constant for the doped material and takes the value of 10−5 S cm−1. It is observed that the AC conductivity starts to increase after the critical frequency value of approximately 103 Hz and increases up to 10−2 S cm−1 due to hopping and tunneling mechanisms caused by space charge polarization accumulated in the local regions at high frequencies. The pure material with an insulating nature also exhibited a typical insulating behavior. Thermal testing showed that nanotube reinforcement increases thermal conductivity in three different directions. DSC thermocurves analysis also revealed that the addition of carbon nanotubes increased the glass transition temperature of the material from 180 °C to 190 °C. The scaling and fractal analysis methods were also applied to obtain hetero morphological structure of materials. The fractal analysis results indicated that carbon nanotube doping to the standard sample increases the coating rates, scalability and heterogeneity of the solid phase surface of the sample. The coating rates of composite surfaces were calculated as 45% and 36%, respectively. Morphology analysis revealed that the probability of finding surface particles for the nanotube-doped sample decreased compared to the undoped sample, but the fractal dimension value increased. While this value was 1.83 in the pure sample, it increased to 1.92 in the nanotube material.
Stability analysis, modulation instability, and the analytical wave solitons to the fractional Boussinesq-Burgers system
Authors: Qawaqneh Haitham , Jari Hassan A , Altalbe Ali , Bekir Ahmet
Published: November 2024

The research is concerned with the novel analytical solitons to the (1+1)-D nonlinear Boussinesq-Burgers System (B-B S) in the sense of a new definition of fractional derivatives. The concerned system is helpful to describes the waves in different phenomenons, including proliferation of waves in shallow water, oceanic waves and many others. Authors gain the solutions involving trigonometric, hyperbolic, and rational functions by using the exp
a
function and the extended sinh-Gordon equation expansion (EShGEE) methods. Fractional derivative provides the better results than the present results. These results are helpful and useful in the different areas of applied sciences, including the optical fibers, telecommunications, plasma physics, fluid dynamics and many more. The solutions are shown by 2-dimensional, 3-dimensional, and contour graphs. The solutions are useful in further studies of the governing model. The stability process is performed to verify that the solutions are exact and accurate. The modulation instability is used to determine the steady-state stable results to the governing equation. The techniques utilized are both simple and effective.
Multipartite entanglement in crossing the quantum critical point
Authors: Sun Hao-Yu , Ge Zi-Yong , Fan Heng
Published: November 2024

We investigate the multipartite entanglement for a slow quantum quench crossing a critical point. We consider the quantum Ising model and the Lipkin-Meshkov-Glick model, which are local and full-connected quantum systems, respectively. The multipartite entanglement is quantified by quantum Fisher information with the generator defined as the operator of the ferromagnetic order parameter. The quench dynamics begins with a ground state in a paramagnetic phase, and then the transverse field is driven slowly to cross a quantum critical point, and ends with a zero transverse field. For the quantum Ising model, based on methods of matrix product states, we calculate the quantum Fisher information density of the final state. Numerical results of both linear and nonlinear quenches show that the quantum Fisher information density of the final state scales as a power law of the quench rate, which overall conforms to the prediction of the Kibble-Zurek mechanism with a small correction. We show that this correction results from the long-range behaviors. We also calculate the quantum Fisher information density in the Lipkin-Meshkov-Glick model. The results show that the scaling of quantum Fisher information in this full-connected system conforms to the Kibble-Zurek mechanism better, since the long-range physics cannot be defined in this nonlocal system. Our results reveal that the multipartite entanglement provides an alternative viewpoint to understand the dynamics of quantum phase transitions, specifically, the nontrivial long-range physics.
Quantum partial coherence measures constructed from Fisher information
Authors: Xuan Dong-Ping , Shen Zhong-Xi , Zhou Wen , Nan Hua , Fei Shao-Ming , Wang Zhi-Xi
Published: November 2024

Quantum mechanics gives a new breakthrough to the field of parameter estimation. In the realm of quantum metrology, the precision of parameter estimation is limited by the quantum Fisher information. We introduce the measures of partial coherence based on (quantum) Fisher information by taking into account the post-selective non-unitary parametrization process. These partial coherence measures present a clear operational interpretation by directly linking the coherence to the parameter estimation accuracy. Furthermore, we explore the distinctions between our partial coherence measure and the quantum Fisher information within the context of unitary parametrization. We provide an analytical expression for the partial coherence measure of two-qubit states. We elucidate the operational significance of the partial coherence measures by establishing the connections between the partial coherence measures and quantum state discrimination.
The effect of atomic concentration on the Cu-Sn IMC formation via molecular dynamic simulation
Authors: Zhang Jicheng , Li Xiaoye , Shi Mang
Published: November 2024

Full intermetallic compound (IMC) solder joints have great potential in high temperature devices because of the higher melting point and excellent mechanical performance. The key to accelerate the fabrication of full IMC solder joints is increasing the diffusion of atoms in solder materials and substrate, which can promote the combination of atoms to form IMC under extra driving forces. We raised a SLID bonding experiment by controlling the distance between Cu pads, aimed at adjusting the atomic concentration in liquid Sn. Results show that after 5 min of bonding at 560 K, when controlling the distance between the Cu pads in a SLID bonding experiment, an IMC thickness of 5.66 μm was achieved, which is 1.4 times higher than with traditional TC bonding technique. To explain this phenomenon, the effects of atomic concentration (measured by relative atomic concentration C) on the atomic diffusivity of atoms in solid Cu and liquid Sn was investigated via molecular dynamics simulations. It shows that the diffusion coefficient of Sn is up to 4.2 times higher when using the displacement control method (C = 0.8, D = 1.18E-06 cm2 s−1) compared to the traditional thermal-compression method (C = 0.91, D = 2.78E-07 cm2 s−1) because of the lower atomic concentration, which proved lower atomic concentration could effectively increase the diffusion coefficient of atoms in liquid Sn. This can be explained by the fact that the Sn atoms are both prevented from being balanced due to volume shrinkage and kept in an active state for diffusion. The conclusions discussed in this paper show us a potential way of increasing the fabrication of full IMC solder joints.
Exploring alternative manufacturing techniques for high-quality solar cell interconnectors: a comparative study of laser cutting and electric discharge machining
Authors: Nasresfahani Mohamad Reza , Asghari Saeed , Ashani Hossein Nazemi , Gorabi Hadi , Joharestani Mahdi Zamani
Published: November 2024

Solar cell interconnectors are typically crafted from thin foils to establish an electrical connection between two components. For limited production of interconnectors with non-standard dimensions, common production methods (such as stamping and sheet metal processes) cannot be utilized. This article examines alternative techniques, such as Fiber Laser Cutting Machine and Electric Discharge Machining (wire cutting). The laser cutting method did not achieve the desired quality standards, leading to the rejection of the interconnectors. Issues such as burning, local melting, and damage to the silver coating were noted at the edges. The melting of the coating layers formed brittle intermetallic compounds, which can cause cracking and reduce weldability and solderability. In contrast, the Electric Discharge Machining method successfully produces interconnectors with complex shapes by stacking layers of foil. This approach results in high-quality interconnectors that showed no signs of burning or local melting on their edges.
Mathematical and Buongiorno nanofluid modelling to predict the thermal and solutal performance of magneto-bioconvective Casson nanofluid flowing in a rotatory disk
Authors: Salawu S O , Shamshuddin MD , Bin Mizan Md Rezwan , Bari Sajjadul
Published: November 2024

This study aims to improve how heat and mass move in systems that use viscoelastic nanofluids under magnetic fields. These systems are commonly used in biotechnology, energy, and medical devices. The significance of this work lies in exploring the steady flow of magnetohydrodynamic (MHD) Casson nanofluids, incorporating the Buongiorno nanofluid model and swimming microorganisms. This research seeks to deepen the understanding of complex fluid behaviours by examining the effects of thermal radiation and chemical diffusion under thermal and solutal convective boundary conditions. The governing equations, which are inherently nonlinear due to multiple physical effects, are converted from two-dimensional partial differential equations (PDEs) to ordinary differential equations (ODEs) using a similarity transformation. A semi-analytical solution is derived using the collocation pseudo-spectral method within the MAPLE computational software. The study investigates how factors like Casson and magnetic parameters, Eckert number, Brownian motion, and thermophoresis affect the flow rate, temperature distribution, species concentration, and microorganism motility. These results are validated by comparing them with established benchmarks. The key findings reveal a pronounced oscillatory behaviour in the temperature profile at higher Eckert number values, while increased Brownian motion and thermophoresis lead to greater nanoparticle dispersion near the disk surface. Higher Lewis and Peclet numbers lead to increased microorganism concentration, demonstrating stronger convective and advective effects. These insights are vital for optimizing drag force, thermal gradients, and mass transfer in engineering applications that involve rotating disks and magnetic fields.
Dynamic modelling and predictive position/force control of a plant-inspired growing robot
Authors: Kalibala Abdonoor , Nada Ayman A , Ishii Hiroyuki , El-Hussieny Haitham
Published: November 2024

This paper presents the development and control of a dynamic model for a plant-inspired growing robot, termed the ‘vine-robot’, using the Euler–Lagrangian method. The unique growth mechanism of the vine-robot enables it to navigate complex environments by extending its body. We derive the dynamic equations of motion and employ model predictive control to regulate the task space position, orientation, and interaction forces. Simulation experiments are conducted to evaluate the performance of the proposed model and control strategy. The results demonstrate that the model effectively achieves sub-millimeter precision in the position control in both static and time varying refrence trajectroies, and sub micronewton in force control.
Long-term patterns of post-fire harvest diverge among ownerships in the Pacific West, U.S.A
Authors: Zuspan Aaron , Reilly Matthew J , Lee E Henry
Published: November 2024

Post-fire harvest (PFH) is a forest management practice designed to salvage value from burned timber, mitigate safety hazards from dead trees, reduce long-term fuels, and prepare sites for replanting. Despite public controversy and extensive ecological research, little is known about how much PFH occurs on private and public lands in the U.S. Pacific West, or how practices changed with shifting forest policy and increasing area burned over the last three decades. We mapped PFH across 2.2 M burned hectares in California, Oregon, and Washington between 1986–2017 and used time series intervention analysis to compare trends in area, rate (% of burned area harvested), and mean patch size between private (0.5 M ha) and federal (1.6 M ha) forest land and across a gradient of burn severity. Harvest rates varied by ownership (4.9% federal, 18.6% private, 8.0% overall), and practices evolved and diverged over the study period. PFH area and rate declined across all ownerships in the mid-1990s during a period of reduced fire activity. As area burned increased between the early 2000s and late 2010s, PFH area rebounded and surpassed late-1980s levels, while rates remained relatively low. On federal lands, PFH practices shifted in the early-to-mid 1990s towards lower rates (10.3%–3.8%) and smaller patches (6.0–3.3 ha), following policy changes and increased litigation. PFH rates on federal lands decreased at all levels of burn severity, with the largest decreases (6.2%–1.2%) in forests with low tree mortality (i.e. fire refugia). Conversely, private PFH rates and mean patch sizes more than doubled in forests burned at very low-to-moderate severity. Our results highlight how PFH practices have shifted with policy, socio-economic pressure, and increasing area burned over 31 years in the Pacific West. A similar area of PFH is now dispersed over larger fires, with practices diverging substantially between ownerships.
Thermodynamics and generalized hydrodynamics of simple integrable QFT in finite volume
Authors: Pawełczyk Jacek
Published: November 2024

We derive thermodynamic (TBA) and general hydrodynamic (GHD) equations corrected by virtual processes for integrable QFT on large but finite size space circle. Obtained TBA’s are solved numerically for the sinh-Gordon model. Complicated Euler scale GHD equations are expanded explicitly for small occupation ratio of virtual quasiparticles. The spectrum of velocities for the linear approximation to GHD is numerically calculated.
Therapeutic efficacy of a probiotic preparation on idiopathic halitosis: a retrospective observational study
Authors: Li 李 Ye Kan 烨侃 , Mou 牟 Fu Yuan 福元 , Qian 钱 Xiao Xian 孝先
Published: November 2024

Idiopathic halitosis is an unusual condition of unclear causes, which has never been thoroughly investigated. We aimed to explore the role of small intestinal bacterial overgrowth (SIBO) in the pathogenesis of idiopathic halitosis, and to evaluate the therapeutic efficacy of a probiotic preparation on this condition. This retrospective observational study included 162 idiopathic halitosis patients and 198 healthy controls (HCs). Halitosis was diagnosed using the organoleptic test, and idiopathic halitosis was diagnosed by excluding known causes. SIBO was identified through the hydrogen/methane lactulose breath test, and accordingly, patients were identified as SIBO-positive or SIBO-negative. Idiopathic halitosis patients were treated with the probiotic preparation Bifidobacterium triple viable capsule for two months, followed by re-evaluation of halitosis and SIBO. This study found that all cases of idiopathic halitosis were extra-oral. The SIBO positivity rate in idiopathic halitosis patients was significantly higher than that in HCs (74.69% [121/162] vs 3.03% [6/198], P < 0.001), with an odds ratio of 94.44% (95% CI: 39.99%–211.35%). After treatment, 80.17% (97/121) of the SIBO-positive patients became SIBO-negative. Moreover, 87.60% (106/121) of the SIBO-positive patients experienced improved halitosis, a rate significantly higher than that observed in SIBO-negative patients (2.75%, 3/41) (P < 0.001). In addition, 98.97% (96/97) of the post-treatment SIBO-negative patients experienced improved halitosis, a rate significantly higher than that of post-treatment sustained SIBO-positive patients (41.67%, 10/24) (P < 0.001). Our findings suggest that idiopathic halitosis is an extra-oral condition which mostly originates from the small intestine. SIBO is one of its underlying causes. The probiotic preparation can effectively improve idiopathic halitosis, probably through its impact on SIBO.
Morphological and structural analysis of monodispersive silica nanoparticles fabricated by stober method for self-cleaning applications
Authors: Pradhan K , Kar J P , Badapanda T , Ghosh S P
Published: November 2024

In this work, silica nanoparticles have been synthesized via the Stober process on a p-type silicon substrate by spin coating method. Structural and morphological studies have been carried out by x-ray diffraction (XRD) and field emission scanning electron microscopy (FESEM). Microstructural analysis by Raman spectroscopy displays a prominent peak at 524 cm−1 which corresponds to crystalline silicon. Further, the elemental study by using energy dispersive x-ray spectroscopy (EDX) confirms the presence of silica with no other impurities. Besides, the wettability property has been studied by contact angle measurement and found to be around 130° for the 6-layer coated thin film which is quasi-super-hydrophobic. The wettability results obtained signify promising properties for the development of self-cleaning devices and applications.
Stochastic black-box optimization using multi-fidelity score function estimator
Authors: Agrawal Atul , Ravi Kislaya , Koutsourelakis Phaedon-Stelios , Bungartz Hans-Joachim
Published: November 2024

Optimizing parameters of physics-based simulators is crucial in the design process of engineering and scientific systems. This becomes particularly challenging when the simulator is stochastic, computationally expensive, black-box and when a high-dimensional vector of parameters needs to be optimized, as e.g. is the case in complex climate models that involve numerous interdependent variables and uncertain parameters. Many traditional optimization methods rely on gradient information, which is frequently unavailable in legacy black-box codes. To address these challenges, we present SCOUT-Nd (Stochastic Constrained Optimization for N dimensions), a gradient-based algorithm that can be used on non-differentiable objectives. It can be combined with natural gradients in order to further enhance convergence properties. and it also incorporates multi-fidelity schemes and an adaptive selection of samples in order to minimize computational effort. We validate our approach using standard, benchmark problems, demonstrating its superior performance in parameter optimization compared to existing methods. Additionally, we showcase the algorithm’s efficacy in a complex real-world application, i.e. the optimization of a wind farm layout.
CSS-Net: a collaborative framework for synthesis and segmentation of missing contrast-enhanced image with error-prediction consistency
Authors: Huang Xiaoyu , Zhang Feixiang , Huang Yong , Xu Kai
Published: November 2024

Automated tumor segmentation plays a critical role in facilitating the diagnosis and assessment of disease progression. Within the realm of tumor segmentation, Contrast-Enhanced (CE) scans are an effective imaging tool that allows for more intuitive observation of tumor characteristics and generally provide better segmentation results compared with Non-CE scans alone. However, CE images are not available in most cases due to the time-consuming and costly need for contrast and repeat scans. To solve this issue, this paper proposes a Collaborative framework for the Synthesis and Segmentation of missing CE images in medical imaging with error-prediction consistency (CSS-Net). CSS-Net simultaneously addresses synthesis and segmentation tasks, generating both the synthesized CE-like images and coarse segmentation results. Subsequently, a multi-layer adaptive feature fusion strategy is utilized to effectively leverage the correlation between these tasks, resulting in refined segmentation results. Additionally, the proposed method incorporates a multi-layer feature fusion block, which adaptively selects features pertinent to segmentation. Furthermore, error-prediction consistency is also introduced between coarse and refined segmentation for regularization, leading to high-performance segmentation results. What’s more, we constructed a multimodal esophageal tumor segmentation dataset with 902 patients and validated it on this dataset and two publicly available multimodal brain tumor datasets. The results indicate that our method achieved Dice scores of 89.04% in esophageal tumor segmentation, 77.01% in whole glioma segmentation, and 91.14% in Vestibular Schwannoma segmentation. This performance surpasses that of segmentation using only available modalities and other image synthesis-based segmentation methods, demonstrating the superior robustness of CSS-Net.
Spatial and demographic heterogeneity in excess mortality in the United States, 2020-2023: a multi-model approach
Authors: Kandula Sasikiran , Kristoffersen Anja Bråthen , Rø Gunnar , LeBlanc Marissa , de Blasio Birgitte Freiesleben
Published: November 2024

In this study, we assessed the overall impact of the Covid-19 pandemic in the United States between 2020 and 2023 through estimates of excess all-cause mortality. Monthly mortality rates over a 19-year period, stratified by age, sex and state of residence were used to forecast expected mortality for the pandemic years. A combination of models – two timeseries, a spatial random effects and a generalized additive -- was used to better capture uncertainty.

Results indicate that US national excess mortality decreased in 2023 to 157 thousand (95% prediction interval: 35K-282K) from 502K (436K-567K), 574K(484K-666K) and 377K (264K-484K) during the years 2020-2022, respectively. Unlike in previous years, deaths with Covid-19 as the underlying-cause-of-death possibly accounted for all excess deaths during 2023. While for the older age groups (75+ years) the year 2020, before vaccines were available, had the highest excess mortality rate, the two younger age groups had the highest excess mortality in 2021. In each age group, women were estimated to have consistently lower excess mortality than men. West Virginia had the highest age-standardized excess mortality among all states in 2021 and 2022.

Our findings demonstrate the value of a multi-model approach in capturing heterogeneity in excess mortality.
Can GPT-4 learn to analyse moves in research article abstracts?
Authors: Yu Danni , Bondi Marina , Hyland Ken
Published: November 2024

One of the most powerful and enduring ideas in written discourse analysis is that genres can be described in terms of the moves which structure a writer’s purpose. Considerable research has sought to identify these distinct communicative acts, but analyses have been beset by problems of subjectivity, reliability, and the time-consuming need for multiple coders to confirm analyses. In this article, we employ the affordances of Generative Pre-trained Transformer 4 (GPT-4) to automate the annotation process by using natural language prompts. Focusing on abstracts from articles in four applied linguistics journals, we devise prompts which enable the model to identify moves effectively. The annotated outputs of these prompts were evaluated by two assessors with a third addressing disagreements. The results show that an eight-shot prompt was more effective than one using two, confirming that the inclusion of examples illustrating areas of variability can enhance GPT-4’s ability to recognize multiple moves in a single sentence and reduce bias related to textual position. We suggest that GPT-4 offers considerable potential in automating this annotation process, when human actors with domain-specific linguistic expertise inform the prompting process.
Smartphone Evaluation of Dynamic and Static Naturalness After Double-Eyelid Blepharoplasty
Authors: Zhang Ce , Gu Qinhao , Chen Lulu , Wang Ji , Shi Hangyan , Li Jingyu , Wu Sufan
Published: November 2024

Background

Recent studies on postoperative measurements for upper blepharoplasty utilized static photographs, which fail to accurately reflect the postsurgical dynamic naturalness of the eyelids.

Objectives

In this study we aimed to analyze dynamic naturalness after double-eyelid blepharoplasty with a simple smartphone method involving slow-motion videos.

Methods

In this prospective observational study we enrolled patients who underwent double-eyelid blepharoplasty with the flexible suspension technique (FST) and rigid fixation technique (RFT), and individuals with congenital double eyelids. Demographic information was collected, and ultra-slow-motion videos of the eye-opening processes were recorded with a smartphone. Four keyframes were selected from each video, from which several parameters were measured. A third party evaluated the degree of naturalness of the photographs and eye-opening videos.

Results

Sixty females (20 per group) were enrolled. The fold-emerging delay (FED) score showed a linear correlation with the mean third-party dynamic naturalness rating (DNR) (R2 = 0.71, P < .0001). The puffy score showed a linear correlation with the mean third-party static naturalness rating (SNR) (R2 = 0.53, P < .0001). For the congenital, FST, and RFT groups, respectively, the FED scores were 62.9 ± 18.9, 52.2 ± 17.7, and 26.3 ± 18.3 (all P < .05); the puffy scores were 49.8 ± 11.9, 55.9 ± 11.0, and 62.6 ± 12.4 (congenital vs FST, P = .055; others P < .05); the mean third-party DNRs were 3.21 ± 0.67, 2.47 ± 0.62, and 1.78 ± 0.74 (all P < .0001); and the mean third-party SNRs were 3.01 ± 0.78, 2.61 ± 0.55, and 2.14 ± 0.69 (all P < .05).

Conclusions

The 2 new indices obtained from the analysis of smartphone-captured ultra-slow-motion videos are good indicators of the dynamic naturalness of double eyelids created with upper blepharoplasty.

Level of Evidence: 4 (Therapeutic)
Graph contrastive learning as a versatile foundation for advanced scRNA-seq data analysis
Authors: Zhang Zhenhao , Liu Yuxi , Xiao Meichen , Wang Kun , Huang Yu , Bian Jiang , Yang Ruolin , Li Fuyi
Published: November 2024

Single-cell RNA sequencing (scRNA-seq) offers unprecedented insights into transcriptome-wide gene expression at the single-cell level. Cell clustering has been long established in the analysis of scRNA-seq data to identify the groups of cells with similar expression profiles. However, cell clustering is technically challenging, as raw scRNA-seq data have various analytical issues, including high dimensionality and dropout values. Existing research has developed deep learning models, such as graph machine learning models and contrastive learning-based models, for cell clustering using scRNA-seq data and has summarized the unsupervised learning of cell clustering into a human-interpretable format. While advances in cell clustering have been profound, we are no closer to finding a simple yet effective framework for learning high-quality representations necessary for robust clustering. In this study, we propose scSimGCL, a novel framework based on the graph contrastive learning paradigm for self-supervised pretraining of graph neural networks. This framework facilitates the generation of high-quality representations crucial for cell clustering. Our scSimGCL incorporates cell-cell graph structure and contrastive learning to enhance the performance of cell clustering. Extensive experimental results on simulated and real scRNA-seq datasets suggest the superiority of the proposed scSimGCL. Moreover, clustering assignment analysis confirms the general applicability of scSimGCL, including state-of-the-art clustering algorithms. Further, ablation study and hyperparameter analysis suggest the efficacy of our network architecture with the robustness of decisions in the self-supervised learning setting. The proposed scSimGCL can serve as a robust framework for practitioners developing tools for cell clustering. The source code of scSimGCL is publicly available at https://github.com/zhangzh1328/scSimGCL.
Model ensembling as a tool to form interpretable multi-omic predictors of cancer pharmacosensitivity
Authors: De Landtsheer Sébastien , Badkas Apurva , Kulms Dagmar , Sauter Thomas
Published: November 2024

Stratification of patients diagnosed with cancer has become a major goal in personalized oncology. One important aspect is the accurate prediction of the response to various drugs. It is expected that the molecular characteristics of the cancer cells contain enough information to retrieve specific signatures, allowing for accurate predictions based solely on these multi-omic data. Ideally, these predictions should be explainable to clinicians, in order to be integrated in the patients care. We propose a machine-learning framework based on ensemble learning to integrate multi-omic data and predict sensitivity to an array of commonly used and experimental compounds, including chemotoxic compounds and targeted kinase inhibitors. We trained a set of classifiers on the different parts of our dataset to produce omic-specific signatures, then trained a random forest classifier on these signatures to predict drug responsiveness. We used the Cancer Cell Line Encyclopedia dataset, comprising multi-omic and drug sensitivity measurements for hundreds of cell lines, to build the predictive models, and validated the results using nested cross-validation. Our results show good performance for several compounds (Area under the Receiver-Operating Curve >79%) across the most frequent cancer types. Furthermore, the simplicity of our approach allows to examine which omic layers have a greater importance in the models and identify new putative markers of drug responsiveness. We propose several models based on small subsets of transcriptional markers with the potential to become useful tools in personalized oncology, paving the way for clinicians to use the molecular characteristics of the tumors to predict sensitivity to therapeutic compounds.
Deep learning model for protein multi-label subcellular localization and function prediction based on multi-task collaborative training
Authors: Bai Peihao , Li Guanghui , Luo Jiawei , Liang Cheng
Published: November 2024

The functional study of proteins is a critical task in modern biology, playing a pivotal role in understanding the mechanisms of pathogenesis, developing new drugs, and discovering novel drug targets. However, existing computational models for subcellular localization face significant challenges, such as reliance on known Gene Ontology (GO) annotation databases or overlooking the relationship between GO annotations and subcellular localization. To address these issues, we propose DeepMTC, an end-to-end deep learning-based multi-task collaborative training model. DeepMTC integrates the interrelationship between subcellular localization and the functional annotation of proteins, leveraging multi-task collaborative training to eliminate dependence on known GO databases. This strategy gives DeepMTC a distinct advantage in predicting newly discovered proteins without prior functional annotations. First, DeepMTC leverages pre-trained language model with high accuracy to obtain the 3D structure and sequence features of proteins. Additionally, it employs a graph transformer module to encode protein sequence features, addressing the problem of long-range dependencies in graph neural networks. Finally, DeepMTC uses a functional cross-attention mechanism to efficiently combine upstream learned functional features to perform the subcellular localization task. The experimental results demonstrate that DeepMTC outperforms state-of-the-art models in both protein function prediction and subcellular localization. Moreover, interpretability experiments revealed that DeepMTC can accurately identify the key residues and functional domains of proteins, confirming its superior performance. The code and dataset of DeepMTC are freely available at https://github.com/ghli16/DeepMTC.
Anifrolumab use in refractory dermatomyositis: a case report and literature review
Authors: Claudio-Oliva Alejandro , Viedma-Martínez Myriam , Gallo-Pineda Gonzalo , Prieto-Rozados Maria , Villegas-Romero Isabel , Jiménez-Gallo David , Linares-Barrios Mario
Published: November 2024

Dermatomyositis (DM) is an autoimmune disease with no established treatment protocol, often unresponsive to traditional immunosuppressants in severe cases. Anifrolumab blocks dysregulated interferon signalling pathways implicated in DM. We report a challenging case of anti-TIF-1-γ refractory DM that responded rapidly to anifrolumab, highlighting its potential as a promising treatment option for refractory cutaneous DM.
Semantic Guided Matting Net
Authors: Song Qing , Sun Wenfeng , Yang Donghan , Hu Mengjie , Liu Chun
Published: November 2024

Human matting refers to extracting human parts from natural images with high quality, including human detail information such as hair, glasses, hats, etc. This technology plays an essential role in image synthesis and visual effects in the film industry. When the green screen is not available, the existing human matting methods need the help of additional inputs (such as trimap, background image, etc.), or the model with high computational cost and complex network structure, which brings great difficulties to the application of human matting in practice. To alleviate such problems, we use a segmentation network as the foundation and use multiple branches to achieve human segmentation, contour detail extraction, and information fusion. We also propose a foreground probability map module, which uses the feature maps in the segmentation network to pre-estimate the foreground probabilities of each pixel and obtain Semantic Guided Matting Net. Under the condition that only a single image is needed as the input, the human matting task can be realized by making full use of the semantic information in the image. We validate our method on the P3M-10k dataset. Compared with the benchmark, our method has made significant improvements in various evaluation indicators.
Effectiveness of Transitional Care in Inflammatory Bowel Disease; Development, Validation, and Initial Outcomes of a Transition Success Score
Authors: van Gaalen Martha A C , van Pieterson Merel , Waaijenberg Petra , Kindermann Angelika , Wolters Victorien M , Dijkstra Alie , van Wering Herbert , Wessels Margreet , de Ridder Lissy , Rizopoulos Dimitris , Derikx C Lauranne A A P , Escher Johanna C ,   the Kids with Crohn’s, Colitis (KiCC) Working Group for Collaborative Paediatric IBD Research in the Netherlands, the Dutch Initiative on Crohn and Colitis (ICC) and Dutch Nurses Network Inflammatory Bowel Disease (NIBD), van Rheenen Patrick F , Teklenburg Sarah T A , van Schaik Fiona D M , van der Woude Janneke C , Hoekstra Jildou , de Leest Marleen , Bakker Carla , Besuijen-Laterveer Elvira M E , Adriaanse Esther , Wisse Jolien , Heida Marloes , Hurkmans Pamela , Boontje Nynke , Toonen Tessa Z , Heida Wendy
Published: November 2024

Background and Aims

The effectiveness of transition programs from pediatric to adult healthcare in adolescents with inflammatory bowel disease (IBD) is not clear, as prospective studies using validated outcome measures for transition are lacking. This study aimed to develop and validate a quantitative Transition Success Score (TSS), and to apply it in a multicenter setting to assess the effectiveness of transitional care.

Methods

The Top 10 outcome items related to a successful transition, identified through an international Delphi study with IBD stakeholders, were integrated into a generic questionnaire, the TSS. In a prospective, multicenter study, the TSS was scored by adult healthcare providers, young adult patients, and caregivers, 9-15 months after transfer of care.

Results

In 7 Dutch hospitals, 160 patients completed the TSS. The mean score was 25 (range 17-27), 25.6% of patients achieving maximum score. Hypothesis testing for construct validity revealed significant associations with characteristics related to transitional care, such as knowledge, independence, and quality of life (p < 0.005). Structural validation indicated the score was most effective at discerning lower levels of transition success. Internal consistency was acceptable (0.64). High disease burden, exacerbation during or after transfer, and certain personality profiles were associated with lower scores.

Conclusions

The TSS serves as a quantitative tool to evaluate the effectiveness of transitional care interventions and to identify IBD patients at risk of encountering challenges during the transition to adult healthcare.
Vascular and inflammatory biomarkers of cardiovascular events in non-steroidal anti-inflammatory drug users
Authors: Vaja Ricky , Ferreira Plinio , Portas Laura , Ahmetaj-Shala Blerina , Cypaite Neringa , Gashaw Hime , Quint Jennifer , Khamis Ramzi , Hartley Adam , MacDonald Thomas M , Mackenzie Isla S , Kirkby Nicholas S , Mitchell Jane A
Published: November 2024

Aims

The Standard care vs. Celecoxib Outcome Trial (SCOT) found similar risk of cardiovascular events with traditional non-steroidal anti-inflammatory drugs (NSAIDs) and the cyclooxygenase-2-selective drug celecoxib. While pre-clinical work has suggested roles for vascular and renal dysfunction in NSAID cardiovascular toxicity, our understanding of these mechanisms remains incomplete. A post hoc analysis of the SCOT cohort was performed to identify clinical risk factors and circulating biomarkers of cardiovascular events in NSAID users.

Methods and results

Within SCOT (7295 NSAID users with osteoarthritis or rheumatoid arthritis), clinical risk factors associated with cardiovascular events were identified using least absolute shrinkage and selection operator regression. A nested case–control study of serum biomarkers including targeted proteomics was performed in individuals who experienced a cardiovascular event within 1 year (n = 49), matched 2:1 with controls who did not (n = 97). Risk factors significantly associated with cardiovascular events included increasing age, male sex, smoking, total cholesterol:HDL ratio ≥5, and aspirin use. Statin use was cardioprotective [odds ratio (OR) 0.68; 95% confidence interval (CI) 0.46–0.98]. There was significantly higher immunoglobulin (Ig)G anti-malondialdehyde-modified LDL (MDA-LDL), asymmetric dimethylarginine (ADMA), and lower arginine/ADMA. Targeted proteomic analysis identified serum growth differentiation factor 15 (GDF-15) as a candidate biomarker [area under the curve of 0.715 (95% CI 0.63–0.81)].

Conclusion

Growth differentiation factor 15 has been identified as a candidate biomarker and should be explored for its mechanistic contribution to NSAID cardiovascular toxicity, particularly given the remarkable providence that GDF-15 was originally described as NSAID-activated gene-1.