bluuebunny/crossref_metadata_2025_split huggingface dataset:
Detection of germline CNVs from gene panel data: benchmarking the state of the art
Authors: Munté Elisabet , Roca Carla , Del Valle Jesús , Feliubadaló Lidia , Pineda Marta , Gel Bernat , Castellanos Elisabeth , Rivera Barbara , Cordero David , Moreno Víctor , Lázaro Conxi , Moreno-Cabrera José Marcos 
Published: December 2024

Abstract
Germline copy number variants (CNVs) play a significant role in hereditary diseases. However, the accurate detection of CNVs from targeted next-generation sequencing (NGS) gene panel data remains a challenging task. Several tools for calling CNVs within this context have been published to date, but the available benchmarks suffer from limitations, including testing on simulated data, testing on small datasets, and testing a small subset of published tools. In this work, we conducted a comprehensive benchmarking of 12 tools (Atlas-CNV, ClearCNV, ClinCNV, CNVkit, Cobalt, CODEX2, CoNVaDING, DECoN, ExomeDepth, GATK-gCNV, panelcn.MOPS, VisCap) on four validated gene panel datasets using their default parameters. We also assessed the impact of modifying 107 tool parameters and identified 13 parameter values that we suggest using to improve the tool F1 score. A total of 66 tool pair combinations were also evaluated to produce better meta-callers. Furthermore, we developed CNVbenchmarker2, a framework to help users perform their own evaluations. Our results indicated that in terms of F1 score, ClinCNV and GATK-gCNV were the best CNV callers. Regarding sensitivity, GATK-gCNV also exhibited particularly high performance. The results presented here provide an evaluation of the current state of the art in germline CNV detection from gene panel data and can be used as a reference resource when using any of the tools.
Integrating genetic algorithms and language models for enhanced enzyme design
Authors: Nana Teukam Yves Gaetan , Zipoli Federico , Laino Teodoro , Criscuolo Emanuele , Grisoni Francesca , Manica Matteo 
Published: December 2024

Abstract
Enzymes are molecular machines optimized by nature to allow otherwise impossible chemical processes to occur. Their design is a challenging task due to the complexity of the protein space and the intricate relationships between sequence, structure, and function. Recently, large language models (LLMs) have emerged as powerful tools for modeling and analyzing biological sequences, but their application to protein design is limited by the high cardinality of the protein space. This study introduces a framework that combines LLMs with genetic algorithms (GAs) to optimize enzymes. LLMs are trained on a large dataset of protein sequences to learn relationships between amino acid residues linked to structure and function. This knowledge is then leveraged by GAs to efficiently search for sequences with improved catalytic performance. We focused on two optimization tasks: improving the feasibility of biochemical reactions and increasing their turnover rate. Systematic evaluations on 105 biocatalytic reactions demonstrated that the LLM–GA framework generated mutants outperforming the wild-type enzymes in terms of feasibility in 90% of the instances. Further in-depth evaluation of seven reactions reveals the power of this methodology to make “the best of both worlds” and create mutants with structural features and flexibility comparable with the wild types. Our approach advances the state-of-the-art computational design of biocatalysts, ultimately opening opportunities for more sustainable chemical processes.
An adaptive enrichment design using Bayesian model averaging for selection and threshold-identification of predictive variables
Authors: Maleyeff Lara , Golchi Shirin , Moodie Erica E M , Hudson Marie 
Published: December 2024

ABSTRACT
Precision medicine is transforming healthcare by offering tailored treatments that enhance patient outcomes and reduce costs. As our understanding of complex diseases improves, clinical trials increasingly aim to detect subgroups of patients with enhanced treatment effects. Biomarker-driven adaptive enrichment designs, which initially enroll a broad population and later restrict to treatment-sensitive patients, are gaining popularity. However, current practice often assumes either pre-trial knowledge of biomarkers or a simple, linear relationship between continuous markers and treatment effectiveness. Motivated by a trial studying rheumatoid arthritis treatment, we propose a Bayesian adaptive enrichment design to identify predictive variables from a larger set of candidate biomarkers. Our approach uses a flexible modeling framework where the effects of continuous biomarkers are represented using free knot B-splines. We then estimate key parameters by marginalizing over all possible variable combinations using Bayesian model averaging. At interim analyses, we assess whether a biomarker-defined subgroup has enhanced or reduced treatment effects, allowing for early termination for efficacy or futility and restricting future enrollment to treatment-sensitive patients. We consider both pre-categorized and continuous biomarkers, the latter potentially having complex, nonlinear relationships to the outcome and treatment effect. Through simulations, we derive the operating characteristics of our design and compare its performance to existing methods.
Joint mirror procedure: controlling false discovery rate for identifying simultaneous signals
Authors: Deng Linsui , He Kejun , Zhang Xianyang 
Published: December 2024

ABSTRACT
In many applications, the process of identifying a specific feature of interest often involves testing multiple hypotheses for their joint statistical significance. Examples include mediation analysis, which simultaneously examines the existence of the exposure-mediator and the mediator-outcome effects, and replicability analysis, aiming to identify simultaneous signals that exhibit statistical significance across multiple independent studies. In this work, we present a new approach called the joint mirror (JM) procedure that effectively detects such features while maintaining false discovery rate (FDR) control in finite samples. The JM procedure employs an iterative method that gradually shrinks the rejection region based on progressively revealed information until a conservative estimate of the false discovery proportion is below the target FDR level. Additionally, we introduce a more stringent error measure known as the composite FDR (cFDR), which assigns weights to each false discovery based on its number of null components. We use the leave-one-out technique to prove that the JM procedure controls the cFDR in finite samples. To implement the JM procedure, we propose an efficient algorithm that can incorporate partial ordering information. Through extensive simulations, we show that our procedure effectively controls the cFDR and enhances statistical power across various scenarios, including the case that test statistics are dependent across the features. Finally, we showcase the utility of our method by applying it to real-world mediation and replicability analyses.
A Bayesian framework for causal analysis of recurrent events with timing misalignment
Authors: Oganisian Arman , Girard Anthony , Steingrimsson Jon A , Moyo Patience 
Published: December 2024

Abstract
Observational studies of recurrent event rates are common in biomedical statistics. Broadly, the goal is to estimate differences in event rates under 2 treatments within a defined target population over a specified follow-up window. Estimation with observational data is challenging because, while membership in the target population is defined in terms of eligibility criteria, treatment is rarely observed exactly at the time of eligibility. Ad hoc solutions to this timing misalignment can induce bias by incorrectly attributing prior event counts and person-time to treatment. Even if eligibility and treatment are aligned, a terminal event process (eg, death) often stops the recurrent event process of interest. In practice, both processes can be censored so that events are not observed over the entire follow-up window. Our approach addresses misalignment by casting it as a time-varying treatment problem: some patients are on treatment at eligibility while others are off treatment but may switch to treatment at a specified time—if they survive long enough. We define and identify an average causal effect estimand under right-censoring. Estimation is done using a g-computation procedure with a joint semiparametric Bayesian model for the death and recurrent event processes. We apply the method to contrast hospitalization rates among patients with different opioid treatments using Medicare insurance claims data.
Mediodorsal thalamus nucleus–medial prefrontal cortex circuitry regulates cost–benefit decision-making selections
Authors: Ding Tong-hao , Hu Yu-ying , Li Jia-wen , Sun Chong , Ma Chao-lin 
Published: December 2024

Abstract
Value-based decision-making involves weighing costs and benefits. The activity of the medial prefrontal cortex reflects cost–benefit assessments, and the mediodorsal thalamus, reciprocally connected with the medial prefrontal cortex, has increasingly been recognized as an active partner in decision-making. However, the specific role of the interaction between the mediodorsal thalamus and the medial prefrontal cortex in regulating the neuronal activity underlying how costs and benefits influence decision-making remains largely unexplored. We investigated this by training the rats to perform a self-determined decision-making task, where longer nose poke durations resulted in correspondingly larger rewards. Our results showed that the inactivation of either the medial prefrontal cortex or the mediodorsal thalamus significantly impaired rat to invest more nose poke duration for larger rewards. Moreover, optogenetic stimulation of the mediodorsal thalamus–medial prefrontal cortex pathway enhanced rats’ motivation for larger rewards, whereas inhibition of this pathway resulted in decreased motivation. Notably, we identified a specific population of neurons in the medial prefrontal cortex that exhibited firing patterns correlated with motivation, and these neurons were modulated by the mediodorsal thalamus–medial prefrontal cortex projection. These findings suggest that the motivation during decision-making is encoded primarily by activity of particular neurons in the medial prefrontal cortex and indicate the crucial role of the mediodorsal thalamus–medial prefrontal cortex pathway in maintaining motivation.
Shared and disorder-specific large-scale intrinsic and effective functional network connectivities in postpartum depression with and without anxiety
Authors: Chen Kexuan , Ma Yingzi , Yang Rui , Li Fang , Li Wei , Chen Jin , Shao Heng , He Chongjun , Chen Meiling , Luo Yuejia , Cheng Bochao , Wang Jiaojian 
Published: December 2024

Abstract
Postpartum depression and postpartum depression with anxiety, which are highly prevalent and debilitating disorders, become a growing public concern. The high overlap on the symptomatic and neurobiological levels led to ongoing debates about their diagnostic and neurobiological uniqueness. Delineating the shared and disorder-specific intrinsic functional connectivities and their causal interactions is fundamental to precision diagnosis and treatment. In this study, we recruited 138 participants including 45 postpartum depression, 31 postpartum depression comorbid with anxiety patients, and 62 healthy postnatal women with age ranging from 23 to 40 years. We combined independent component analysis, resting-state functional connectivity, and Granger causality analysis to reveal the abnormal intrinsic functional couplings and their causal interactions in postpartum depression and postpartum depression comorbid with anxiety from a large-scale brain network perspective. We found that they exhibited widespread abnormalities in intrinsic and effective functional network connectivities. Importantly, the intrinsic and effective functional network connectivities within or between the fronto-parietal network, default model network, ventral and dorsal attention network, sensorimotor network, and visual network, especially the functional imbalances between primary and association cortices could serve as effective neural markers to differentiate postpartum depression, postpartum depression comorbid with anxiety, and healthy controls. Our findings provide the initial evidence for shared and disorder-specific intrinsic and effective functional network connectivities for postpartum depression and postpartum depression comorbid with anxiety, which provide an underlying neuropathological basis for postpartum depression or postpartum depression comorbid with anxiety to facilitate precision diagnosis and therapy in future studies.
The relevance of Marshall’s thought today: from methodological eclecticism to his sociological outlook
Authors: Pietrini Filippo 
Published: December 2024

Abstract
This article leverages the reinterpretations of Marshall emerged from the 1970s onwards to elucidate his philosophical vision, notably in relation to the application of models in economics and to his outlook on ethical progress. The literature showed how Marshall’s cognitive theory can be linked both to his method in studying market equilibria and to his view of social and industrial evolution (Dardi, 2016, 2019; Raffaelli et al., 1994b; Raffaelli, 2003). The focus of the present work is on the elements of his philosophical grounding discernible in both the logical connections mentioned above. The contribution to the literature consists of an interpretation of the lecture on American industry in Whitaker, (1975a) as an insight of a sociologist of knowledge ante litteram. Thus the paper broadens, with respect to Dardi, 2016, the field of Whewell’s influence on Alfred Marshall: from the methodology (Whewell’s idea of the dualistic nature of mathematics linked to the Cambridge mathematical intuitionism) to the causal relationship between industry and character and to Marshall’s keen eye for sociological facets. In many respects a contemporary Marshall comes to the fore, proving particularly valuable for those still grappling with epistemological issues in the social sciences.
Global digital networks
Authors: Zook Matthew , Grote Michael 
Published: December 2024

Abstract
The rapid evolution of digital technologies over the past decades has induced profound economic and social transformations. Economic geography faces the ongoing challenge of assimilating these changes into existing theories that elucidate the dynamics of the global economy. In response, we present the Global Digital Networks (GDN) framework, drawing inspiration from established analytical instruments like Global Production Networks (GPN) and Global Financial Networks (GFN). GDN centres on three key economic materialities—people, things, and places—intertwined with territorially grounded practices of data generation and enhancement. We identify four enhancement types—singularisation, association, centralisation and fractionalisation—driving a cyclical process shaping complex networks across territories. Governance structures, encompassing national regulations, platform systems, and firm governance, play a pivotal role. The GDN cycle, exemplified through diverse territorial scenarios, underscores the intricate interplay of data generation, enhancement and governance structures in delineating global economic networks.
Multi-modal artificial intelligence algorithm for the prediction of left atrial low-voltage areas in atrial fibrillation patient based on sinus rhythm electrocardiogram and clinical characteristics: a retrospective, multicentre study
Authors: Tao Yirao , Zhang Deyun , Pang Naidong , Geng Shijia , Tan Chen , Tian Ying , Hong Shenda , Liu XingPeng 
Published: December 2024

Abstract

Aims
We aimed to develop an artificial intelligence (AI) algorithm capable of accurately predicting the presence of left atrial low-voltage areas (LVAs) based on sinus rhythm electrocardiograms (ECGs) in patients with atrial fibrillation (AF).

Methods and results
The study included 1133 patients with AF who underwent catheter ablation procedures, with a total of 1787 12-lead ECG images analysed. Artificial intelligence-based algorithms were used to construct models for predicting the presence of LVAs. The DR-FLASH and APPLE clinical scores for LVAs prediction were calculated. A receiver operating characteristic (ROC) curve and a calibration curve were used to evaluate model performance. Multicentre validation included 92 AF patients from five centres, with a total of 174 ECGs. The data obtained from the participants were split into training (n = 906), validation (n = 113), and test sets (n = 114). Low-voltage areas were detected in 47.4% of all participants. Using ECG alone, the convolutional neural network (CNN) model achieved an area under the ROC curve (AUROC) of 0.704, outperforming both the DR-FLASH score (AUROC = 0.601) and the APPLE score (AUROC = 0.589). Two multimodal AI models, which integrated ECG images and clinical features, demonstrated higher diagnostic accuracy (AUROC 0.816 and 0.796 for the CNN-Multimodal and CNN-Random Forest-Multimodal models, respectively). Our models also performed well in the multicentre validation dataset (AUROC 0.711, 0.785, and 0.879 for the ECG alone, CNN-Multimodal, and CNN-Random Forest-Multimodal models, respectively).

Conclusion
The multimodal AI algorithm, which integrated ECG images and clinical features, predicted the presence of LVAs with a higher degree of accuracy than ECG alone and the clinical LVA scores.
Physical link between effective viscosity and electrical resistivity for dislocation creep in upper mantle and its application in Northwest Xinjiang, China
Authors: Li Man , Xu Yixian , Liu Lian , Yang Bo , Zhang Yi , Zhu Yixin , Liu Shuyu 
Published: December 2024

SUMMARY
Effective viscosity of the upper mantle is a critical parameter for comprehending the dynamics of the lithosphere and plate tectonics. In recent years, magnetotelluric (MT) surveys have gained attention as a potential tool for determining viscosity. However, a direct physical basis for the relationship between effective viscosity and electrical resistivity still needs to be improved. To address this issue, we have established the basis that connects electrical resistivity and effective viscosity under different thermochemical conditions and the principle of electrical neutrality. The creep and electrical conductions of rocks in the lithospheric mantle are all the thermally activated atomic-scale random motion of particles in solids or melts, controlled by the slowest and fastest particles, respectively. Due to the sizeable electrostatic interaction energy, the concentrations of the two particles must always satisfy the charge-neutral condition. Hence, we derived two ideal models for anhydrous and hydrous olivine. Then, to demonstrate their applicability, we converted the resistivity cross-section constructed from long-period MT data to the effective viscosity distribution in the Tarim–Tianshan–Junggar area. We found that the results matched those obtained by the previous method well. Overall, this study provides valuable insights into the potential of MT surveys for determining the effective viscosity of the upper mantle.
Effects of subduction interface locking distributions on tsunami hazard: a case study on the Hikurangi/Tonga-Kermadec subduction zones
Authors: Hughes Laura , Lane Emily M , Power William , Savage Martha K , Arnold Richard , Howell Andrew , Liao Yi-Wun Mika , Williams Charles , Shaw Bruce , Fry Bill , Nicol Andy 
Published: December 2024

SUMMARY
Subduction zones have the greatest potential to generate large earthquakes and tsunamis. However, when undertaking Probabilistic Tsunami Hazard Assessments (PTHAs), subduction zones are a significant source of epistemic uncertainty. Therefore, understanding how the spatial distribution of elastic strain accumulation on the subduction interface influences the tsunami hazard is important for providing comprehensive hazard assessments, as well as quantifying uncertainty. This is especially important if the spatial locking distribution is undefined, and if it changes through time. Physics-based earthquake simulators allow different interpretations of the subduction interface locking distribution to be modelled, and how this influences the long-term seismicity, and the tsunami hazard, can be explored. Using three physics-based synthetic earthquake catalogues, generated by the earthquake simulator RSQSim, we analysed the tsunami hazard in Aotearoa/New Zealand. Three alternative representations of the subduction interface locking distribution along the Hikurangi Subduction Margin and the Tonga-Kermadec Subduction Zone were specified in the simulator to generate the catalogues. We modelled the tsunamis generated by $M_W\, \gt $8.0 earthquakes from each of the catalogues and undertook PTHAs. These assessments show that patches of high slip-deficit, both along strike and dip of the subduction interface, increase the tsunami hazard at the coast. Locking along the shallowest segments of the subduction interface also significantly increases the tsunami hazard. Our results show that careful consideration of the locking distribution in physical models is necessary before using them for PTHAs. They also show that by analysing multiple physical models of subduction zones, uncertainty in hazard assessments caused by the unresolved interface properties can also begin to be quantified.
Recent court ruling could increase the size and administrative complexity of the 340B program
Authors: Nikpay Sayeh , Bruno John P , Carey Colleen 
Published: December 2024

Abstract
The 340B program allows certain hospitals and clinics to use outpatient drugs purchased at substantial discounts on insured patients, generating profits to fund care. The size of these profits depends on the number of prescriptions filled by participating hospital or clinics’ insured patients that also meet the Health Resources and Services Agency's definition of an eligible patient. A recent court case has challenged the Agency's longstanding definition of a patient, resulting in new definition that could significantly expand the size of the program and create conflicts when an insured patient satisfies the new definition for more than one hospital or clinic participating in the program. We use Medicare Part D data from 2018 to simulate the proportion of prescription drug fills eligible for 340B discounts and total program spending under both existing and new definitions. We found that the new definition could increase the share of 340B-eligible fills in Medicare Part D by 25%, from 12% of fills to 16%, and that the share of fills subject to a conflict could double, from 1% of fills to 1%-2%. Our results suggest that the new definition could increase covered entities’ 340B profits by roughly a third.