bluuebunny/crossref_metadata_2025_split huggingface dataset:
Constructing Deterrence
Authors: Edelman R David 
Published: February 2024

AbstractChapter 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

Abstract
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

Abstract
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

Abstract

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

ABSTRACTA 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

Abstract
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

Abstract

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

Abstract
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

Abstract
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

Abstract
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

Abstract

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

Abstract

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

Abstract

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

SUMMARY
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.