bluuebunny/crossref_metadata_2025 huggingface dataset:
Magneto-ionic modulation of the interlayer exchange interaction in synthetic antiferromagnets
Authors: Syskaki Maria-Andromachi , Dohi Takaaki , Bednarz Beatrice , Filnov Sergei Olegovich , Kasatikov Sergey Alexeyevich , Bhukta Mona , Smekhova Alevtina , Pachat Rohit , van der Jagt Johannes Wilhelmus , Ono Shimpei , Ravelosona Dafiné , Langer Jürgen , Kläui Mathias , Herrera Diez Liza , Jakob Gerhard
Published: February 2024

The electric-field control of magnetism is a highly promising and potentially effective approach for realizing energy-efficient applications. Recent interest has focused on the magneto-ionic effect in synthetic antiferromagnets, driven by its potential to enable high-density data storage devices with ultra-low power consumption. However, the underlying mechanism responsible for the magneto-ionic effect on the interlayer exchange coupling remains elusive. In our work, we find that the modulation of the interlayer exchange coupling is highly sensitive to the thickness of the ferromagnetic layer. We have identified that the changes in the interlayer exchange coupling induced by the gate voltage can be associated with the magneto-ionic effects on the top ferromagnetic layer of the synthetic antiferromagnet. The direct contact between the high ion mobility oxide and the top ferromagnetic layer plays a crucial role in facilitating these effects, largely modifying the anisotropy of the layers. Our findings highlight the important role of magneto-ionic control over the properties of the top ferromagnetic layer in governing the observed modifications in the interlayer exchange coupling. This study provides crucial insight into the intricate interplay between stack structure and magneto-ionic effect on magnetic properties in synthetic antiferromagnetic thin film systems.
Bill Weis (1959–2023): Pioneering structural biologist and biochemist who revolutionized our understanding of cell adhesion and Wnt signaling
Authors: Peifer Mark , Dunn Alexander R.
Published: February 2024

In October 2023, cell biology lost one of its brightest stars, Bill Weis, gone too soon at the age of 64. Bill was a masterful biochemist and structural biologist who made landmark contributions to a remarkable number of fields, most notably cell–cell adhesion, Wnt signaling, and signaling by G-protein coupled receptors.
Quantifying the Tension between Cosmological Models and JWST Red Candidate Massive Galaxies
Authors: Wang Jun-Chao , Huang Zhi-Qi , Huang Lu , Liu Jianqi
Published: February 2024

We develop a Python tool to estimate the tail distribution of the number of dark matter halos beyond a mass threshold and in a given volume in a light-cone. The code is based on the extended Press–Schechter model and is computationally efficient, typically taking a few seconds on a personal laptop for a given set of cosmological parameters. The high efficiency of the code allows a quick estimation of the tension between cosmological models and the red candidate massive galaxies released by the James Webb Space Telescope, as well as scanning the theory space with the Markov Chain Monte Carlo method. As an example application, we use the tool to study the cosmological implication of the candidate galaxies presented in Labbé et al. The standard Λ cold dark matter (ΛCDM) model is well consistent with the data if the star formation efficiency can reach ∼0.3 at high redshift. For a low star formation efficiency ϵ ∼ 0.1, the ΛCDM model is disfavored at ∼2σ–3σ confidence level.
One in-situ extraction algorithm for monitoring bunch-by-bunch profile in the storage ring
Authors: Wu Ruizhe , Zhao Yunkun , Tang Leilei , Wang Jigang , Lu Ping , Sun Baogen
Published: February 2024

As the brightness of synchrotron radiation (SR) light sources improves, the operation stability of light sources is weakened. To explore various beam instability related issues in light sources, one transverse beam diagnostics system for bunch-by-bunch (BbB) profile measurement has been established at Hefei Light Source-II (HLS-II). In this paper, one in-situ extraction algorithm in the data processing backend of the system is developed for BbB profiles, so as to provide important beam information of the machine operation in time.
Gaussian wavelet basis expansion based phase noise suppression method for coherent optical OFDM systems
Authors: Zhou He , Fang Xi , Zhang Song , Wang Yunzhang , Jin Jifang , Ding Ding
Published: February 2024

In this paper, we developed a phase noise suppression method based on Gaussian wavelet basis expansion (GWBE) for coherent optical orthogonal frequency-division-multiplexing (CO-OFDM) systems. Compared with traditional phase noise suppression methods, the GWBE method significantly enhances the system’s robustness to laser phase noise, providing improved phase noise suppression, especially in scenarios with high linewidth. Additionally, the method demonstrates excellent performance in 32-QAM and 64-QAM. By employing the proposed GWBE method, the impact of laser phase noise on CO-OFDM can be effectively mitigated.
Test beam results of a fluorescence-based monitor for ultra-high dose rates
Authors: Trigilio A. , De Gregorio A. , De Simoni M. , Franciosini G. , Garbini M. , Magi M. , Marafini M. , Mirabelli R. , Muscato A. , Patera V. , Schiavi A. , Toppi M. , Traini G. , Sarti A.
Published: February 2024

In recent years, there has been a significant focus on improving the effectiveness of radiotherapy (RT) and particle therapy in treating tumors while minimizing damage to healthy tissue. A promising development is offered by the observation of the so-called FLASH effect, where ultra-high dose rates delivered in a short time have shown to protect healthy tissues while maintaining anti-tumor efficacy. However, conventional detectors face challenges in monitoring charged beams at these ultra-high dose rates due to non-linear effects. To address this challenge, the FlashDC (Flash Detector beam Counter) has been developed. It uses air fluorescence to monitor beam fluence and spatial distribution in real-time with high accuracy and minimal impact on treatment delivery. This innovative detector offers a linear response for various charged beams, dose rates, and energies, making it a cost-effective solution. Multiple prototypes have been developed and optimized using Monte Carlo simulations. The analysis of data from recent test beam campaigns with electrons delivered at FLASH intensities has demonstrated a linear correlation between the detector signal and the delivered dose per pulse, confirming that fluorescence can be used for beam monitoring in FLASH-RT studies. This contribution introduces the FlashDC monitor, discusses its expected performance, and presents preliminary test beam results obtained with electron beams in FLASH mode.
Automatic tuning of PID controllers using deep recurrent neural networks with pruning based on tracking error
Authors: Ahmadi Aghil , Mahboobi Esfanjani Reza
Published: February 2024

Although PID controllers are common in industry, they are
often poorly tuned; especially, in uncertain environments. Modern
industries, with increasing complexity, motivate us to employ new
intelligent methods in order to extend PID controllers beyond their
usual capabilities. In this paper, an advanced machine learning
scheme is utilized to improve PID controllers; for the first time, a
deep dynamic neural network is employed to tune online the
parameters of the traditional PID controller in order to overcome
the effects of uncertainties in the closed-loop control system. To
reduce the computational burden of deep recurrent neural network, a
novel structural learning technique is applied to optimize the
configuration. Unlike existing pruning methods, the network is
pruned based on the values of neurons and the total value of the
corresponding layer. Simulation of a benchmark CSTR system
demonstrate that the proposed scheme performs more efficiently
compared to a shallow network tuner, in the presence of
uncertainties.
Multi-objectives optimization can contribute to conservation and restoration priority setting in dryland region
Authors: Sun Siqi , Liu Xin , Wang Cong , Jiang Wei , Ma Ruiming , Ao Nen , Guo Jianying , Lü Yihe
Published: February 2024

The ecological conservation and restoration of drylands is a global challenge, which is always under resources constraints and in competition with land developments. Here, we aim to identify ecological conservation and restoration priority areas based on the objectives of maximizing ecological values and minimizing opportunity costs with zonation as a heuristic spatial optimization tool. We take the Inner Mongolia (IM) of China as a typical dryland region. Results reveal that the efficiency of multi-objectives for ecological restoration is often better than a single objective. IM should focus on ecological conservation supplemented by restoration. Conserving 36.9% and restoring 13.1% of IM can provide an average of 72% of ecosystem functions while consuming 20% of opportunity costs. Our framework aligns with the need for nature-based solutions and rationalizes the allocation of resources for ecological conservation and restoration projects in complex dryland regions.
The importance of the local structure of fractal aggregates
Authors: Botet Robert , Rannou Pascal , Tazaki Ryo
Published: February 2024

The pair correlation function, g(r), is a fundamental descriptor of the inner structure of fractal aggregates of monomers. It provides a natural tool for studying physical properties involving two-point interaction (e.g. optics of aggregates). Several domains of distances between pairs of monomers have been identified. The fractal domain (in which g(r) is a power-law) is generally dominant for large aggregates. We show here that the local behavior of g(r)—involving monomers tangent to a given monomer—is necessary for most of the quantitative applications, even if that local domain is not directly related to fractal morphology. We derive a simple generic pair correlation function for fractal aggregates, depending on three structural parameters only: the fractal dimension, df
; the prefactor, kf
; the local mean coordination number,

Z
ˉ

. Unlike the fractal dimension, the prefactor and the mean coordination number are not universal since they depend on many parameters of the generation process. We discuss the impact of the definite shape of g(r) on the aggregate structure factor profile (a measurable quantity through small-angle x-ray scattering). Improvement from the new g(r) shape is also illustrated deriving an analytical expression of the geometric cross section, G, of aggregates for fractal dimension

<

2

. Accuracy is checked by comparing with numerical data from aggregates of fractal dimension

d
f

=
1.9

. On the up-to-date analytical expression of G, the contributions of the short- and long-range behaviours of g(r) are well separated, and it is clear that the local behavior of the pair correlation function is required to obtain accurate values of the geometric cross section. Thus, in addition to the fractal structure of aggregates, the local structure of aggregates (to which the prefactor kf
and the g(r) divergence at short distances are related) also appears important to accurately describe their physical features.
Operator algebra generalization of a theorem of Watrous and mixed unitary quantum channels
Authors: Kribs David W , Levick Jeremy , Pereira Rajesh , Rahaman Mizanur
Published: February 2024

We establish an operator algebra generalization of Watrous’ theorem (Watrous 2009 Quantum Inf. Comput.
9 403–413) on mixing unital quantum channels (completely positive trace-preserving maps) with the completely depolarizing channel, wherein the more general objects of focus become (finite-dimensional) von Neumann algebras, the unique trace preserving conditional expectation onto the algebra, the group of unitary operators in the commutant of the algebra, and the fixed point algebra of the channel. As an application, we obtain a result on the asymptotic theory of quantum channels, showing that all unital channels are eventually mixed unitary. We also discuss the special case of the diagonal algebra in detail, and draw connections to the theory of correlation matrices and Schur product maps.
A long-term storable gel-laden chip composite built in a multi-well plate enabling in situ cell encapsulation for high-throughput liver model
Authors: Kim Min Kyeong , Park Jubin , Tak Sungho , Paek Kyurim , Bang Geul , Woo Sang-Mi , Ravichandran Naresh Kumar , Hong Won Gi , Kang Hyun-Wook , Kim Hyang , Bae Ji Yong , Kim Jeong Ah
Published: February 2024

Hydrogels are widely used as scaffold materials for constructing in vitro three-dimensional microphysiological systems. However, their high sensitivity to various external cues hinders the development of hydrogel-laden, microscale, and high-throughput chips. Here, we have developed a long-term storable gel-laden chip composite built in a multi-well plate, which enables in situ cell encapsulation and facilitates high-throughput analysis. Through optimized chemical crosslinking and freeze-drying method (C/FD), we have achieved a high-quality of gel-laden chip composite with excellent transparency, uniform porosity, and appropriate swelling and mechanical characteristics. Besides collagen, decellularized extracellular matrix with tissue-specific biochemical compound has been applied as chip composite. As a ready-to-use platform, in situ cell encapsulation within the gel has been achieved through capillary force generated during gel reswelling. The liver-mimetic chip composite, comprising HepG2 cells or primary hepatocytes, has demonstrated favorable hepatic functionality and high sensitivity in drug testing. The developed fabrication process with improved stability of gels and storability allows chip composites to be stored at a wide range of temperatures for up to 28 d without any deformation, demonstrating off-the-shelf products. Consequently, this provides an exceptionally simple and long-term storable platform that can be utilized for an efficient tissue-specific modeling and various biomedical applications.
3D bioprinting of mouse pre-osteoblasts and human MSCs using bioinks consisting of gelatin and decellularized bone particles
Authors: Kara Özenler Aylin , Distler Thomas , Akkineni Ashwini Rahul , Tihminlioglu Funda , Gelinsky Michael , Boccaccini Aldo R
Published: February 2024

One of the key challenges in biofabrication applications is to obtain bioinks that provide a balance between printability, shape fidelity, cell viability, and tissue maturation. Decellularization methods allow the extraction of natural extracellular matrix, preserving tissue-specific matrix proteins. However, the critical challenge in bone decellularization is to preserve both organic (collagen, proteoglycans) and inorganic components (hydroxyapatite) to maintain the natural composition and functionality of bone. Besides, there is a need to investigate the effects of decellularized bone (DB) particles as a tissue-based additive in bioink formulation to develop functional bioinks. Here we evaluated the effect of incorporating DB particles of different sizes (≤45 and ≤100 μm) and concentrations (1%, 5%, 10% (wt %)) into bioink formulations containing gelatin (GEL) and pre-osteoblasts (MC3T3-E1) or human mesenchymal stem cells (hTERT-MSCs). In addition, we propose a minimalistic bioink formulation using GEL, DB particles and cells with an easy preparation process resulting in a high cell viability. The printability properties of the inks were evaluated. Additionally, rheological properties were determined with shear thinning and thixotropy tests. The bioprinted constructs were cultured for 28 days. The viability, proliferation, and osteogenic differentiation capacity of cells were evaluated using biochemical assays and fluorescence microscopy. The incorporation of DB particles enhanced cell proliferation and osteogenic differentiation capacity which might be due to the natural collagen and hydroxyapatite content of DB particles. Alkaline phosphatase activity is increased significantly by using DB particles, notably, without an osteogenic induction of the cells. Moreover, fluorescence images display pronounced cell-material interaction and cell attachment inside the constructs. With these promising results, the present minimalistic bioink formulation is envisioned as a potential candidate for bone tissue engineering as a clinically translatable material with straightforward preparation and high cell activity.
Effect of surface strengthening by ultrasonic shot peening on TC17 alloy
Authors: Tan Mingxin , Cai Jin , Qu Jinglong , Xie Xingfei , Lv Shaomin
Published: February 2024

This paper investigates the effects of ultrasonic shot peening (USP) on surface morphology, microstructure, and residual stress field distribution of TC17 alloy under different process parameters. The aim is to reveal the surface strengthening mechanism of TC17 alloy caused by USP. The results suggest that the use of the 2.5 mm projectile diameter leads to an increase in surface roughness, plastic deformation, and a deeper grain refinement layer compared to the 1.5 mm projectile diameter. Additionally, it results in a greater depth of the compressive residual stress layer and maximum compressive residual stress. The crack initiation sites under two projectile diameters are located below the compressive residual stress layer. The USP treatment introduces compressive residual stress on the surface, inhibiting the initiation of surface cracks, and the deeper compressive residual stress layer offsets the early fatigue failure caused by higher roughness.
Electronic structures and quantum capacitance of twisted mixed-dimensional van der Waals heterostructures of graphene/C2H based on tight-binding model
Authors: Xin Baojuan , Li Boyan , Yang Wen , Li Luyan , Dong Hong , Cheng Yahui , Liu Hui , Wang Wei-Hua , Lu Feng
Published: February 2024

Constructing twisted mixed dimensional graphene-based van der Waals heterostructure (vdWH) is an effective strategy to manipulate the electronic structures and improve the quantum capacitance (C
q) of graphene. In this work, mixed dimensional vdWH of graphene/C2H has been proposed owing to similar Dirac semimetal character of one-dimensional C2H with that of graphene. Meanwhile, the influence of twisting angle (θ) and interlayer interaction strength on the electronic structures and the C
q of the MD vdWH are systemically explored based on tight binding model. With the fitted hopping integral parameters, it is found that the linear dispersion of the graphene is basically preserved but the bandwidth is decreased with modulating twisting angle and interlayer interaction, and the C
q of mixed dimensional vdWH is improved 5–19 times compared with graphene at zero bias. Moreover, the compressed strain could enhance the C
q of mixed dimensional vdWH to 74.57 μF cm−2 at zero bias and broaden the low working voltage window of mixed-dimensional vdWH with considerable C
q. Our results provide suitable tight-binding model parameters and theoretical guidance for exploring the twisted MD vdWH of graphene/C2H and offer an effective strategy to modulate the electronic structures and the C
q of graphene through constructing the MD vdWH.
Fast prototype and rapid construction of three-dimensional and multi-scaled pitcher for controlled drainage by systematic biomimicry
Authors: Shen Tao , Li Ning , Liu Shijie , Yu Cunlong , Zhang Chengqi , Yang Kang , Li Xingfei , Fang Ruochen , Jiang Lei , Dong Zhichao
Published: February 2024

Biomimetic materials that use natural wisdom to solve practical problems are developing rapidly. The trend for systematic biomimicry is towards in-situ characterization of natural creatures with high spatial resolutions. Furthermore, rapid reconstruction of digital twin models with the same complex features as the prototype is indispensable. However, it faces bottlenecks and limits in fast characterization and fabrication, precise parameter optimization, geometric deviations control, and quality prediction. To solve these challenges, here, we demonstrate a state-of-the-art method taking advantage of micro-computed tomography and three-dimensional printing for the fast characterization of the pitcher plant Nepenthes x ventrata and fabrication of its biomimetic model to obtain a superior drainage controller with multiscale structures with precise surface morphology optimization and geometric deviation control. The film-rupture-based drainage dynamic and mechanisms are characterized by x-ray and high-speed videography, which determines the crucial structures for unique directional drainage. Then the optimized artificial pitchers are further developed into sustained drainage devices with novel applications, such as detection, reaction, and smoke control.
Rethinking Cyber Warfare
Authors: Edelman R David
Published: February 2024

Fifteen years into the era of cyber warfare, are we any closer to understanding the role a major cyberattack would play in international relations—or to preventing one? Spanning disciplines and enriched by practitioner insights, Rethinking Cyber Warfare provides a novel understanding of the role that digital disruption plays in contemporary international security. The work focuses on the critical phenomenon of major cyberattacks against wired societies, a significant capability that, despite considerable state planning for such an incident, has received comparatively little scholarly consideration. Starting with a reconsideration of the central tenets that have shaped global powers’ policies to date, it explores in-depth what forces in the international system might durably restrain their use. Arming the reader with the key technological and historical context to make sense of cyberattacks, it considers how deterrence, international law, and normative taboos operate today to shape whether and how states think about causing this kind of disruption—and how soon those forces might combine to rethink those decisions entirely. The result is a comprehensive look at one of the most pressing issues in international security that also illuminates a new pathway for managing one of its greatest sources of instability.
Defining and Studying Cyberattacks
Authors: Edelman R David
Published: February 2024

Chapter 1 opens with the claim that Estonia was a rehearsal—and in some ways the beginning of a new epoch in international security, when Kremlin-aligned actors hobbled that small Baltic nation’s economic and communications capabilities in an act of apparent political protest. It outlines the central topic and scope of the book, oriented around the question: Which, if any, forces in the international system might restrain state use of cyberattacks, despite the strategic advantage they confer? It then provides a formal definition of cyberattacks—distinct from espionage or theft, these acts of coercion or sabotage disrupting or destroying infrastructure to significant national security effect. Critically for the subsequent analysis, this definition differentiates cyberattacks (as used here) from other merely unfriendly or criminally illegal activities like spying or extortion. The chapter then explores the many of the key assumptions that motivated early analysis and in particular state doctrine on cybersecurity—on the role of nonstate actors; the ability to attribute attacks; the speed of necessary response; and even the concept of a cyberwar distinct from other state hostilities—and in each case finds them deeply flawed. With the hindsight of fifteen years, it is now possible to see just how differently the landscape for cyberattacks has evolved from prior estimations, and in turn, providing a new lens through which to interpret the historical record and assess the capacity for restraint.
Constructing Deterrence
Authors: Edelman R David
Published: February 2024

Chapter 4 chapter argues that cyberattacks deprive states of the ability to make meaningful rationalist calculations, rendering cyber capabilities both poor deterrents and difficult to deter. Consequently, restraining cyberattacks requires more than amassing greater, similar capabilities in the manner of most conventional and nuclear deterrence. Instead, it argues that a different and novel strategy is necessary—and already in use: a strategy of structural deterrence, shaping the international environment through alliances and law to favor their strengths within an overall deterrence relationship. In exhausting the explanatory power of conventional deterrence concepts, it suggests that we are presently observing a form of cyber-deterrence, but not of the sort upon which military science literature narrowly focuses. It argues that states are in fact presently trying to escape this “deterrence purgatory” by short-circuiting their opponents’ deterrence calculations through a broader project revolving around whether acceptable responses to cyberattacks are military in nature. This is the missing link in rationalist restraint, and if solved for, would make deterrence matter as a potential source thereof. The argument here is not abstract; states are aware of this crucial ambiguity and are seeking, in a novel realm of interstate competition, to set the parameters for future deterrence of cyberattacks. In this respect, the contest in which states are presently engaged to restrain one another’s use of cyberattacks is one of rationalist dissuasion through neoliberal means.
CRISPR-DIPOFF: an interpretable deep learning approach for CRISPR Cas-9 off-target prediction
Authors: Toufikuzzaman Md , Hassan Samee Md Abul , Sohel Rahman M
Published: February 2024

CRISPR Cas-9 is a groundbreaking genome-editing tool that harnesses bacterial defense systems to alter DNA sequences accurately. This innovative technology holds vast promise in multiple domains like biotechnology, agriculture and medicine. However, such power does not come without its own peril, and one such issue is the potential for unintended modifications (Off-Target), which highlights the need for accurate prediction and mitigation strategies. Though previous studies have demonstrated improvement in Off-Target prediction capability with the application of deep learning, they often struggle with the precision-recall trade-off, limiting their effectiveness and do not provide proper interpretation of the complex decision-making process of their models. To address these limitations, we have thoroughly explored deep learning networks, particularly the recurrent neural network based models, leveraging their established success in handling sequence data. Furthermore, we have employed genetic algorithm for hyperparameter tuning to optimize these models’ performance. The results from our experiments demonstrate significant performance improvement compared with the current state-of-the-art in Off-Target prediction, highlighting the efficacy of our approach. Furthermore, leveraging the power of the integrated gradient method, we make an effort to interpret our models resulting in a detailed analysis and understanding of the underlying factors that contribute to Off-Target predictions, in particular the presence of two sub-regions in the seed region of single guide RNA which extends the established biological hypothesis of Off-Target effects. To the best of our knowledge, our model can be considered as the first model combining high efficacy, interpretability and a desirable balance between precision and recall.
Multiple and Optimal Screening Subset: a method selecting global characteristic congeners for robust foodomics analysis
Authors: Xu Rui , Zhang Huan , Crowder Michael W , Zhu Jiangjiang
Published: February 2024

Metabolomics and foodomics shed light on the molecular processes within living organisms and the complex food composition by leveraging sophisticated analytical techniques to systematically analyze the vast array of molecular features. The traditional feature-picking method often results in arbitrary selections of the model, feature ranking, and cut-off, which may lead to suboptimal results. Thus, a Multiple and Optimal Screening Subset (MOSS) approach was developed in this study to achieve a balance between a minimal number of predictors and high predictive accuracy during statistical model setup. The MOSS approach compares five commonly used models in the context of food matrix analysis, specifically bourbons. These models include Student’s t-test, receiver operating characteristic curve, partial least squares-discriminant analysis (PLS-DA), random forests, and support vector machines. The approach employs cross-validation to identify promising subset feature candidates that contribute to food characteristic classification. It then determines the optimal subset size by comparing it to the corresponding top-ranked features. Finally, it selects the optimal feature subset by traversing all possible feature candidate combinations. By utilizing MOSS approach to analyze 1406 mass spectral features from a collection of 122 bourbon samples, we were able to generate a subset of features for bourbon age prediction with 88% accuracy. Additionally, MOSS increased the area under the curve performance of sweetness prediction to 0.898 with only four predictors compared with the top-ranked four features at 0.681 based on the PLS-DA model. Overall, we demonstrated that MOSS provides an efficient and effective approach for selecting optimal features compared with other frequently utilized methods.
Phenotype prediction from single-cell RNA-seq data using attention-based neural networks
Authors: Mao Yuzhen , Lin Yen-Yi , Wong Nelson K Y , Volik Stanislav , Sar Funda , Collins Colin , Ester Martin
Published: February 2024

Motivation

A patient’s disease phenotype can be driven and determined by specific groups of cells whose marker genes are either unknown or can only be detected at late-stage using conventional bulk assays such as RNA-Seq technology. Recent advances in single-cell RNA sequencing (scRNA-seq) enable gene expression profiling in cell-level resolution, and therefore have the potential to identify those cells driving the disease phenotype even while the number of these cells is small. However, most existing methods rely heavily on accurate cell type detection, and the number of available annotated samples is usually too small for training deep learning predictive models.

Results

Here, we propose the method ScRAT for phenotype prediction using scRNA-seq data. To train ScRAT with a limited number of samples of different phenotypes, such as coronavirus disease (COVID) and non-COVID, ScRAT first applies a mixup module to increase the number of training samples. A multi-head attention mechanism is employed to learn the most informative cells for each phenotype without relying on a given cell type annotation. Using three public COVID datasets, we show that ScRAT outperforms other phenotype prediction methods. The performance edge of ScRAT over its competitors increases as the number of training samples decreases, indicating the efficacy of our sample mixup. Critical cell types detected based on high-attention cells also support novel findings in the original papers and the recent literature. This suggests that ScRAT overcomes the challenge of missing marker genes and limited sample number with great potential revealing novel molecular mechanisms and/or therapies.

Availability and implementation

The code of our proposed method ScRAT is published at https://github.com/yuzhenmao/ScRAT.
Estimating the effect of latent time-varying count exposures using multiple lists
Authors: Won Jung Yeon , Elliott Michael R , Sanchez-Vaznaugh Emma V , Sánchez Brisa N
Published: February 2024

A major challenge in longitudinal built-environment health studies is the accuracy of commercial business databases that are used to characterize dynamic food environments. Different databases often provide conflicting exposure measures on the same subject due to different source credibilities. As on-site verification is not feasible for historical data, we suggest combining multiple databases to correct the bias in health effect estimates due to measurement error in any 1 datasource. We propose a joint model for the time-varying health outcomes, observed count exposures, and latent true count exposures. Our model estimates the time-specific quality of sources and incorporates time dependence of true count exposure by Poisson integer-valued first-order autoregressive process. We take a Bayesian nonparametric approach to flexibly account for location-specific exposures. By resolving the discordance between different databases, our method reduces the bias in the longitudinal health effect of the true exposures. Our method is demonstrated with childhood obesity data in California public schools with respect to convenience store exposures in school neighborhoods from 2001 to 2008.
Power and Purpose in an Immigration Removal Centre
Authors: Aitken Dominic
Published: February 2024

How is power manifest inside an immigration removal centre (IRC), and does the basic function of the institution align with staff’s sense of purpose? Drawing on interviews with employees at Brook House IRC, I argue that power is simultaneously present, absent and elsewhere. Staff acknowledge the presence of power in their ‘immigration prison’, but routinely feel an absence of authority, and note that decisions about immigration cases are made elsewhere. I then analyze how employees speak about the purpose of their work, which they see as providing both security and welfare. I conclude that the twin realities of power and purpose, security and welfare, create a dilemma for staff, IRCs and the liberal state as a whole.
Long-Term Protective Effect of Tuberculosis Preventive Therapy in a Medium/High Tuberculosis Incidence Setting
Authors: Teixeira Leidy Anne Alves , Santos Braulio , Correia Marcelo Goulart , Valiquette Chantal , Bastos Mayara Lisboa , Menzies Dick , Trajman Anete
Published: February 2024

Background

The duration of the protective effect of tuberculosis preventive therapy (TPT) is controversial. Some studies have found that the protective effect of TPT is lost after cessation of therapy among people with human immunodeficiency virus (HIV) in settings with very high tuberculosis incidence, but others have found long-term protection in low-incidence settings.

Methods

We estimated the incidence rate (IR) of new tuberculosis disease for up to 12 years after randomization to 4 months of rifampin or 9 months of isoniazid, among 991 Brazilian participants in a TPT trial in the state of Rio de Janeiro, with an incidence of 68.6/100 000 population in 2022. The adjusted hazard ratios (aHRs) of independent variables for incident tuberculosis were calculated.

Results

The overall tuberculosis IR was 1.7 (95% confidence interval [CI], 1.01– 2.7) per 1000 person-years (PY). The tuberculosis IR was higher among those who did not complete TPT than in those who did (2.9 [95% CI, 1.3–5.6] vs 1.1 [.4–2.3] per 1000 PY; IR ratio, 2.7 [1.0–7.2]). The tuberculosis IR was higher within 28 months after randomization (IR, 3.5 [95% CI, 1.6–6.6] vs 1.1 [.5–2.1] per 1000 PY between 28 and 143 months; IR ratio, 3.1 [1.2–8.2]). Treatment noncompletion was the only variable associated with incident tuberculosis (aHR, 3.2 [95% CI, 1.1–9.7]).

Conclusions

In a mostly HIV-noninfected population, a complete course of TPT conferred long-term protection against tuberculosis.
Unsupervised framework for evaluating and explaining structural node embeddings of graphs
Authors: Dehghan Ashkan , Siuta Kinga , Skorupka Agata , Betlen Andrei , Miller David , Kamiński Bogumił , Prałat Paweł
Published: February 2024

An embedding is a mapping from a set of nodes of a network into a real vector space. Embeddings can have various aims like capturing the underlying graph topology and structure, node-to-node relationship, or other relevant information about the graph, its subgraphs or nodes themselves. A practical challenge with using embeddings is that there are many available variants to choose from. Selecting a small set of most promising embeddings from the long list of possible options for a given task is challenging and often requires domain expertise. Embeddings can be categorized into two main types: classical embeddings and structural embeddings. Classical embeddings focus on learning both local and global proximity of nodes, while structural embeddings learn information specifically about the local structure of nodes’ neighbourhood. For classical node embeddings, there exists a framework which helps data scientists to identify (in an unsupervised way) a few embeddings that are worth further investigation. Unfortunately, no such framework exists for structural embeddings. In this article, we propose a framework for unsupervised ranking of structural graph embeddings. The proposed framework, apart from assigning an aggregate quality score for a structural embedding, additionally gives a data scientist insights into properties of this embedding. It produces information which predefined node features the embedding learns, how well it learns them, and which dimensions in the embedded space represent the predefined node features. Using this information, the user gets a level of explainability to an otherwise complex black-box embedding algorithm.
Management of cardiac emergencies in women: a clinical consensus statement of the Association for Acute CardioVascular Care (ACVC), the European Association of Percutaneous Cardiovascular Interventions (EAPCI), the Heart Failure Association (HFA), and the European Heart Rhythm Association (EHRA) of the ESC, and the ESC Working Group on Cardiovascular Pharmacotherapy
Authors: Sambola Antonia , Halvorsen Sigrun , Adlam David , Hassager Christian , Price Susanna , Rosano Giuseppe , Schiele Francois , Holmvang Lene , de Riva Marta , Rakisheva Amina , Sulzgruber Patrick , Swahn Eva
Published: February 2024

Cardiac emergencies in women, such as acute coronary syndromes, acute heart failure, and cardiac arrest, are associated with a high risk of adverse outcomes and mortality. Although women historically have been significantly underrepresented in clinical studies of these diseases, the guideline-recommended treatment for these emergencies is generally the same for both sexes. Still, women are less likely to receive evidence-based treatment compared to men. Furthermore, specific diseases affecting predominantly or exclusively women, such as spontaneous coronary dissection, myocardial infarction with non-obstructive coronary arteries, takotsubo cardiomyopathy, and peripartum cardiomyopathy, require specialized attention in terms of both diagnosis and management. In this clinical consensus statement, we summarize current knowledge on therapeutic management of these emergencies in women. Key statements and specific quality indicators are suggested to achieve equal and specific care for both sexes. Finally, we discuss several gaps in evidence and encourage further studies designed and powered with adequate attention for sex-specific analysis.
Noncoding RNA Contribution to Aging and Lifespan
Authors: Ugalde Alejandro P , Roiz-Valle David , Moledo-Nodar Lucas , Caravia Xurde M , Freije José M P , López-Otín Carlos
Published: February 2024

Aging is a multifactorial process characterized by an age-related decline in organismal fitness. This deterioration is the major risk factor for chronic diseases such as cardiovascular pathologies, neurodegeneration, or cancer, and it represents one of the main challenges of modern society. Therefore, understanding why and how we age would be a fundamental pillar to design strategies to promote a healthy aging. In the last decades, the study of the molecular bases of disease has been revolutionized by the discovery of different types of noncoding RNAs (ncRNAs) with regulatory potential. In this work, we will review the implication of ncRNAs in aging, with the aim to provide a first approach to the different aging-associated ncRNAs, their mechanism of action, and their potential relevance as therapeutic targets and disease biomarkers.
The Complexity of Center of Pressure Positions During Quiet Stance and Its Relationship to Cognition, Aging, and Falls
Authors: Langeard Antoine , Milot Emma , Quarck Gaëlle , Stoffregen Thomas
Published: February 2024

Background

In a secondary analysis of data taken from a publicly available database, we examined cognitive performance, postural sway, and relations between them for 4 groups: younger and older individuals with versus without a recent history of falls. Our objective was to compare linear versus nonlinear measures of postural activity as post hoc predictors of cognitive performance and falling.

Methods

We evaluated standing body sway in 147 participants (18–85-years old) over 60 seconds, separately with eyes-open and with eyes-closed. We evaluated cognitive performance using portions of the Trail Making Test. We evaluated postural activity in terms of standard deviation, velocity, and amplitude of the Center of Pressure (CoP). Separately, we used detrended fluctuation analysis (DFA) to examine the complexity of CoP displacements. Using analysis of variance, we conducted separate analyses of cognitive performance and postural activity comparing Younger and Older Adults and Non-fallers and Fallers, taking into account Vision (eyes-closed vs open) and the direction of postural movements (Anteroposterior vs Mediolateral) while also controlling for participants’ characteristics. We used moderation analyses to evaluate whether relationships between Trail Making Test scores and the linear and nonlinear outcomes were moderated by Age group or Fall status.

Results

For postural activity, only DFA differed between Non-fallers and Fallers. Older adults exhibited increased complexity associated with better processing speed function, while fallers show an opposite association, relying on processing speed to increase postural rigidity instead of facilitating adaptive control of balance.

Conclusions

We conclude that DFA can provide information regarding postural activity and cognitive performance that cannot be obtained from more traditional, linear measures of postural activity and that DFA may be a valuable tool for assessing fall risk.
Association of Testosterone and Sex Hormone-Binding Globulin With All-Cause and Cardiovascular Disease Mortality in Older Chinese Men
Authors: Li Mei Jiao , Jiang Chao Qiang , Jin Ya Li , Zhu Tong , Zhu Feng , Zhang Wei Sen , Xu Lin
Published: February 2024

Background

The associations of high and low testosterone with all-cause and cardiovascular disease (CVD) mortality risk in men are conflicting. Our objective was to examine associations of total testosterone, free testosterone, bioavailable testosterone, and sex hormone-binding globulin (SHBG) with all-cause and CVD mortality in older Chinese men.

Methods

Total testosterone and SHBG were assayed, and free testosterone and bioavailable testosterone were calculated using Vermeulen formula. Cox proportional hazards regression was used to assess the associations with risks of all-cause and CVD mortality, giving hazard ratios (HRs) and 95% confidence intervals (CIs).

Results

Of 3 948 men aged 50+ years, 949 deaths (312 CVD) occurred during an average 10.5-year follow-up. After multivariable adjustments, the highest, versus the third, quartile of total testosterone and free testosterone were associated with higher all-cause mortality risk (1.17 [0.97–1.41] and 1.45 [1.20–1.74], respectively), whereas free testosterone was associated with higher CVD mortality risk (1.88 [1.33–2.66]). Similar positive associations were found for bioavailable testosterone and all-cause mortality risk (1.27 [1.05–1.54]). Lower SHBG (quartile 1 vs quartile 3) was associated with higher all-cause and CVD mortality risk (1.25 [1.04–1.52] and 1.28 [1.08–1.52], respectively). Consistent associations were observed in relatively healthy men and men excluded death during the first year.

Conclusions

Higher total testosterone, free testosterone, and bioavailable testosterone were associated with higher all-cause mortality in older men, higher free testosterone was associated with higher CVD mortality whilst lower SHBG was associated with higher all-cause and CVD mortality. Clarification and confirmation of causality require further mechanistic studies.
Bidirectional Relationships and Mediating Effects Between Social Isolation, Loneliness, and Frailty in Chinese Older Adults
Authors: Pan Chaoping
Published: February 2024

Background and Objectives

Social isolation (SI) and loneliness are key factors that contribute to frailty among older adults. Current estimates regarding how frailty affects SI and loneliness and how SI and loneliness affect frailty may be flawed due to reverse causality. This study aimed to investigate the bidirectional relationships and mediating effects among SI, loneliness, and frailty among older adults in China.

Research Design and Methods

The study analyzed data from 6 waves of the Chinese Longitudinal Healthy Longevity Survey conducted between 2002 and 2018. The sample included individuals aged 65 and older. The General Cross-Lagged Panel Model was used to account for confounding factors and reveal mediating effects.

Results

Our findings specifically indicate a direct effect of SI on frailty, although suggesting that loneliness may indirectly affect frailty through its influence on SI. Additionally, frailty can lead to increased SI and loneliness.

Discussion and Implications

SI and loneliness are strongly intertwined with frailty among older adults in China. To prevent the development of frailty, public health initiatives should prioritize reducing SI among older adults. Furthermore, efforts to decrease frailty levels can yield positive outcomes by mitigating both SI and loneliness among this population.
Coseismic slip distribution of the 2023 earthquake doublet in Turkey and Syria from joint inversion of Sentinel-1 and Sentinel-2 data: an iterative modelling method for mapping large earthquake deformation
Authors: Chen Jianlong , Zhou Yu
Published: February 2024

Interferometric synthetic aperture radar (InSAR) decorrelation that creates great challenges to phase unwrapping has been a critical issue for mapping large earthquake deformation. Some studies have proposed a ‘remove-and-return model’ solution to tackle this problem, but it has not been fully validated yet, and therefore has rarely been applied to real earthquake scenarios. In this study, we use the 2023 Mw 7.8 and 7.6 earthquake doublet in Turkey and Syria as a case example to develop an iterative modelling method for InSAR-based coseismic mapping. We first derive surface deformation fields using Sentinel-1 offset tracking and Sentinel-2 optical image correlation, and invert them for an initial coseismic slip model, based on which we simulate InSAR coseismic phase measurements. We then remove the simulated phase from the actual Sentinel-1 phase and conduct unwrapping. The simulated phase is added back to the unwrapped phase to produce the final phase measurements. Comparing to the commonly used unwrapping method, our proposed approach can significantly improve coherence and reduce phase gradients, enabling accurate InSAR measurements. Combining InSAR, offset tracking and optical image correlation, we implement a joint inversion to obtain an optimal coseismic slip model. Our model shows that slip on the Çardak Fault is concentrated on a ∼100 km segment; to both ends, slip suddenly diminished. On the contrary, rupture on the East Anatolian Fault Zone propagated much longer as its geometry is fairly smooth. The iterative coseismic modelling method is proven efficient and can be easily applied to other continental earthquakes.