bluuebunny/crossref_metadata_2025_split huggingface dataset:
Current trends in global quantum metrology
Authors: Mukhopadhyay Chiranjib , Montenegro Victor , Bayat Abolfazl 
Published: January 2025

Abstract
Quantum sensors are now universally acknowledged as one of the most promising near-term quantum technologies. The traditional formulation of quantum sensing introduces a concrete bound on ultimate precision through the so-called local sensing framework, in which a significant knowledge of prior information about the unknown parameter value is implicitly assumed. Moreover, the framework provides a systematic approach for optimizing the sensing protocol. In contrast, the paradigm of global sensing aims to find a precision bound for parameter estimation in the absence of such prior information. In recent years, vigorous research has been pursued to describe the contours of global quantum estimation. Here, we review some of these emerging developments. These developments are both in the realm of finding ultimate precision bounds with respect to appropriate figures of merit in the global sensing paradigm, as well as in the search for algorithms that achieve these bounds. We categorize these developments into two largely mutually exclusive camps; one employing Bayesian updating and the other seeking to generalize the frequentist picture of local sensing towards the global paradigm. In the first approach, in order to achieve the best performance, one has to optimize the measurement settings adaptively. In the second approach, the measurement setting is fixed, however the challenge is to identify this fixed measurement optimally.
The impact of copper additive content on the synthesis of SiC from rice husks
Authors: Trung Kien Kieu Do , Ngoc Minh Huynh 
Published: January 2025

Abstract
Silicon carbide is a compound that is usually synthesized by sintering silicon dioxide and carbon in an arc furnace at high temperatures. This method requires sophisticated technology and equipment, which restricts the widespread application of silicon carbide. As a result, researchers have turned to biomass materials for its synthesis, with rice husk being the primary source. However, the efficiency of synthesizing SiC from rice husks tends to be relatively low. This study investigates the effect of copper additives on the synthesis efficiency of SiC. A mixture of rice husks, silica gel, and copper was prepared in varying ratios and then sintered at 1100 °C in a CO2 atmosphere to promote SiC formation. The SiC content, mineralogical composition, and microstructure of the products were analyzed using ISO 21068-2:2008, Fourier Transform Infrared Spectroscopy, x-ray Diffraction, Scanning Electron Microscopy, and Energy Dispersive x-ray Spectroscopy. The results indicate that a rice husks-to-copper ratio of 1:2 significantly improved SiC production. Despite the enhancement, copper introduced unwanted impurities into the final products. These impurities were successfully removed by washing the SiC with hydrofluoric acid, resulting in a SiC content of 76.25% by weight. The synthesized SiC exhibited both α-SiC and β-SiC allotropic forms, contributing to improved mechanical strength and heat resistance of the material.
Optimizing compressive strength prediction of pervious concrete using artificial neural network
Authors: Wijekoon Sathushka Heshan Bandara , Janarth Asoharasa , Dharmar Joseph , Vinojan Perinparasa , Sathiparan Navaratnarajah , Subramaniam Daniel Niruban 
Published: January 2025

Abstract
The prediction of compressive strength is crucial, as it is influenced by various mix parameters such as aggregate size, aggregate-to-cement ratio, and compaction. Accurate forecasting of strength ensures optimized mix designs, enhancing both performance and material efficiency in construction projects. The novelty of this study lies in integrating machine learning techniques to predict the compressive strength of pervious concrete, incorporating these key parameters to improve predictive accuracy and facilitate more precise and sustainable mix design choices. For the experimental study, 600 samples were prepared with varying aggregate-to-cement ratios (3.0–5.0), compaction (0–60 blows from standard proctor rammer), and aggregate size (4.75–25 mm) and monitored for porosity and compressive strength. A modified Ryshkewitch model is assessed alongside experimental evaluations to improve mix design optimization. The effect of parameter variability on compressive strength is assessed to investigate the uncertainty propagation. Key uncertainties are highlighted by sensitivity analysis, and output distributions are produced by Monte Carlo simulations, reducing uncertainty is essential for practical applications, as it guarantees that compressive strength forecasts remain constant across a range of materials and environmental circumstances. In addition, machine learning and neural network models were analyzed for accuracy. Incorporating porosity in compressive strength prediction enhanced the R2 of empirical and neural network models from 0.63 and 0.78 to 0.92, respectively, while uncertainty was comparable to experimental observations. Aggregate size-based models improved performance by more than 0.95 for all cases, insisting on the dominant impact of aggregate size on prediction models. The research concludes that the optimized mix designs not only improve compressive strength but also promote sustainability by reducing material waste and enhancing the durability of pervious concrete. These findings provide valuable insights for more efficient and environmentally friendly concrete mix designs in urban infrastructure projects.
Conclusion
Authors: Ju Angela 
Published: January 2025

Abstract
This final chapter ties together the main findings and broader impacts of the book in Brazil, as well as in other similar immigrant-receiving countries. It pays special attention to theoretical and policy implications of the study for immigration as Brazil seeks to enhance its image on the world stage and addresses the impact of events that have occurred since my fieldwork in São Paulo, such as the presidency of Jair Bolsonaro and the COVID-19 pandemic. This chapter concludes the book by discussing recommendations for future research, especially in the context of Luiz Inácio Lula da Silva’s return to the Brazilian presidency, and the limitations of the study.
Tidy-Mindedness
Authors: Queloz Matthieu 
Published: January 2025

Abstract
This chapter assesses the merits of the main non-foundationalist rival to the needs-based approach to concept appraisal, which also escapes the trilemma and revives something of the spirit of the Vienna Circle: the tidy-minded approach that seeks conceptual authority by eliminating theoretical vices from our conceptual repertoire. Various kinds of theoretical vices—including superficiality, inconsistency, incoherence, and incongruence—are explicated, thereby setting the stage for later chapters. Different forms of the tidy-minded approach are then considered, but they are all found wanting as general answers to the authority question. To supplement more theoretically virtuous concepts with the grounds on which they can claim more authority, and to discriminate contexts in which these grounds are given from contexts in which they are lacking, a more comprehensive approach is needed—one that considers not just how concepts relate to each other, but how they relate to us and our concerns.
Boosting GPT models for genomics analysis: generating trusted genetic variant annotations and interpretations through RAG and Fine-tuning
Authors: Lu Shuangjia , Cosgun Erdal 
Published: January 2025

Abstract

Motivation
Large language models (LLMs) have acquired a remarkable level of knowledge through their initial training. However, they lack expertise in particular domains such as genomics. Variant annotation data, an important component of genomics, is crucial for interpreting and prioritizing disease-related variants among millions of variants identified by genetic sequencing. In our project, we aimed to improve LLM performance in genomics by adding variant annotation data to LLMs by retrieval-augmented generation (RAG) and fine-tuning techniques.

Results
Using RAG, we successfully integrated 190 million highly accurate variant annotations, curated from five major annotation datasets and tools, into GPT-4o. This integration empowers users to query specific variants and receive accurate variant annotations and interpretations supported by advanced reasoning and language understanding capabilities of LLMs. Additionally, fine-tuning GPT-4 on variant annotation data also improved model performance in some annotation fields, although the accuracy across more fields remains suboptimal. Our model significantly improved the accessibility and efficiency of the variant interpretation process by leveraging LLM capabilities. Our project also revealed that RAG outperforms fine-tuning in factual knowledge injection in terms of data volume, accuracy, and cost-effectiveness. As a pioneering study for adding genomics knowledge to LLMs, our work paves the way for developing more comprehensive and informative genomics AI systems to support clinical diagnosis and research projects, and it demonstrates the potential of LLMs in specialized domains.

Availability and implementation
We used publicly available datasets as detailed in the paper, which can be provided upon request.
A regularized Bayesian Dirichlet-multinomial regression model for integrating single-cell-level omics and patient-level clinical study data
Authors: Guo Yanghong , Yu Lei , Guo Lei , Xu Lin , Li Qiwei 
Published: January 2025

ABSTRACT
The abundance of various cell types can vary significantly among patients with varying phenotypes and even those with the same phenotype. Recent scientific advancements provide mounting evidence that other clinical variables, such as age, gender, and lifestyle habits, can also influence the abundance of certain cell types. However, current methods for integrating single-cell-level omics data with clinical variables are inadequate. In this study, we propose a regularized Bayesian Dirichlet-multinomial regression framework to investigate the relationship between single-cell RNA sequencing data and patient-level clinical data. Additionally, the model employs a novel hierarchical tree structure to identify such relationships at different cell-type levels. Our model successfully uncovers significant associations between specific cell types and clinical variables across three distinct diseases: pulmonary fibrosis, COVID-19, and non-small cell lung cancer. This integrative analysis provides biological insights and could potentially inform clinical interventions for various diseases.
Coronary microvascular dysfunction by positron emission tomography and outcomes in patients after cardiac transplantation
Authors: Abadie Bryan , Elghoul Yasmine , Prakash Sakthi Surya , Besir Besir , Ziada Khaled , Jacob Miriam , Bhattacharya Sanjeeb , Bhat Pavan , Starling Randall C , Tang W H Wilson , Jaber Wael 
Published: January 2025

Abstract

Aims
Cardiac allograft vasculopathy (CAV) affects both epicardial and microvascular coronary arteries, however, few studies have characterized microvascular dysfunction in this population. Several prior studies have shown that positron emission tomography/computed tomography (PET/CT) can be used to screen for epicardial CAV, however, the clinical implications of abnormal blood flow in the absence of epicardial CAV are unknown. Our study sought to assess the prognostic implications of microvascular dysfunction and its subtypes, endogen/functional and classical/structural, using PET/CT in cardiac transplant patients without epicardial CAV.

Methods and results
Transplant patients with no prior history of CAV and normal myocardial perfusion imaging were included. Patients were then classified by the presence of coronary microvascular dysfunction (CMD) (myocardial flow reserve &lt; 2.0); patients with CMD were further subcategorized into endogen/functional (stress myocardial blood flow ≥ 1.7 mL/min/g) and classical/structural (stress myocardial blood flow &lt; 1.7 mL/min/g). The primary outcomes were all-cause mortality and a composite of all-cause mortality, heart failure hospitalization, acute coronary syndrome, revascularization, and re-transplantation. Three hundred fifty-six patients met the inclusion criteria. CMD was present in 141 (39.6%) patients, of which 112 (31.4%) had endogen/functional CMD and 29 (8.1%) had classical/structural CMD. After multivariable adjustment, endogen/functional CMD was associated with a higher rate of the composite outcome (HR 2.39, 95% CI 1.32–4.29, P = 0.004) and all-cause mortality (HR 2.98, 95% CI 1.34–6.64, P = 0.008). Classical/structural CMD was not associated with the primary composite outcome (HR 0.92, 95% CI 0.27–3.17, P = 0.893) or all-cause mortality (HR 1.22, 95% CI 0.263–5.69, P = 0.797).

Conclusion
In cardiac transplant patients with no history of CAV and normal myocardial perfusion, an endogen/functional pattern of CMD is associated with higher rate of adverse events and death. This association was not present in patients with a classical/structural CMD pattern. Incorporating endogen/microvascular dysfunction assessment in PET/CT reporting may identify a higher-risk group hereto now considered low risk.
Yellow Pages: Experimental Evidence on Household-Firm Connections
Authors: Dillon Brian , Aker Jenny C , Blumenstock Joshua E 
Published: January 2025

Abstract
We study the impact of a low-cost intervention to reduce information frictions in rural markets by randomly assigning small and medium enterprises in Tanzania to be listed in a telephone directory. The listed firms expand their communication networks, increase sales, and make greater use of mobile money, with positive spillovers to firms in the same village. Estimated effects are larger and more statistically precise for firms that are more productive at baseline, consistent with consumers seeking out better firms when search costs fall. We find no evidence of negative between-village spillovers on unlisted firms.
Meeting the challenges of online teacher education in Madagascar
Authors: Raminoarivony Mirany 
Published: January 2025

Abstract
This study examines attitudes and engagement with online teaching among English teacher educators in a Malagasy pre-service teacher training institute during and after the coronavirus disease (COVID) epidemic. It explores how teachers responded to the COVID-related challenges and opportunities independently, showing various attitudes and approaches toward online teaching. Using a qualitative questionnaire, the data collected were analyzed implementing the General Inductive Approach. Findings reveal individual teachers’ varied usages of social media and Moodle in reconnecting with teaching and students over distances, coping with a relative unpreparedness in a normal situation, exacerbated by the epidemic. The study identifies key challenges such as uncertainties during COVID, insufficient training, material and human resources in educational technology, and high internet costs. They demonstrated a lack of a community of practice that develops teachers’ online teaching competences. It offers recommendations for similar low-resource contexts in Madagascar and the global South.
3-D variational inference-based double-difference seismic tomography method and application to the SAFOD site, California
Authors: Yang Hao , Zhang Xin , Zhang Haijiang 
Published: January 2025

SUMMARY
Seismic tomography is used to image subsurface structures at various scales, accomplished by solving a nonlinear and non-unique inverse problem. It is therefore important to quantify velocity model uncertainties for accurate earthquake locations and geological interpretations. Monte Carlo sampling techniques are usually used for this purpose, but those methods are computationally intensive, especially for large data sets or high-dimensional parameter spaces. In comparison, Bayesian variational inference provides a more efficient alternative by delivering probabilistic solutions through optimization. The method has been proven to be efficient in 2-D tomographic problems. In this study, we apply variational inference to solve 3-D double-difference (DD) seismic tomographic system using both absolute and differential traveltime data. Synthetic tests demonstrate that the new method can produce more accurate velocity models than the original DD tomography method by avoiding regularization constraints, and at the same time provides more reliable uncertainty estimates. Compared to traditional checkerboard resolution tests, the resulting uncertainty estimates provide a better measure for the reliability of the solution. We further apply the new method to data recorded by a local dense seismic array around the San Andreas Fault Observatory at Depth (SAFOD) site along the San Andreas Fault (SAF) at Parkfield. Similar to previous studies, the obtained velocity models show significant velocity contrasts across the fault. More importantly, the new method produces velocity uncertainties of less than 0.34 km s−1 for ${{{{V}}}_p}$ and 0.23 km s−1 for ${{{{V}}}_s}$. We therefore conclude that variational inference provides an effective tool for solving 3-D seismic tomographic problems and quantifying model uncertainties.
Resting-State Functional Connectivity of the Cerebellum Differs Between Persons With Inflammatory Bowel Disease and Healthy Controls in Relation to Executive Function
Authors: Kornelsen Jennifer , McIver Theresa A , Marrie Ruth Ann , Patel Ronak , Bernstein Charles N 
Published: January 2025

Lay Summary
Persons with inflammatory bowel disease exhibit a weaker relationship between executive function and functional connectivity of cerebellar regions (VIIa Crus I and VIIa Crus II) to cortical areas involved in visual processing compared to healthy counterparts with comparable performance.
Clinical validation of an innovative dried whole-blood spot method to quantify simultaneously vancomycin and creatinine in adult patients
Authors: Hassanzai M , Bahmany S , van Onzenoort H A W , van Oldenrijk J , Koch B C P , de Winter B C M 
Published: January 2025

Abstract

Background
A drawback of vancomycin use is the need for therapeutic drug monitoring and renal function monitoring. Traditional blood sampling involves drawing blood through a venepuncture. An alternative method, dried blood spot (DBS) sampling allows for self-sampling at home.

Objectives
To clinically validate a DBS method for simultaneous monitoring of vancomycin and creatinine.

Methods
Hospitalized adults treated with intravenous vancomycin were included (trial registration NCT05257070). Blood sampling consisted of one venepuncture and one finger prick. Whole-blood DBS samples from patients were obtained by applying one drop of whole blood onto Whatman 903 filtrate paper. Bland–Altman analyses were used to assess the agreement and bias between the two measurements. Patients were asked to state their preferences for one of the two sampling methods.

Results
The study involved a final analysis of 39 patient samples for the clinical validation of vancomycin and 46 patient samples for the clinical validation of creatinine. The difference between plasma and DBS concentrations was ≤20% for 77% of the vancomycin samples, the mean bias was −0.1379% (95% limit of agreement −5.899–5.623). The difference between plasma and DBS concentrations was ≤20% for 89% of the creatinine samples, the mean bias was 2.656% (95% limit of agreement −26.16–31.47). Most patients (18 out of 31) preferred a finger prick over a venepuncture and 12 patients indicated no preference.

Conclusions
This is the first study that successfully clinically validated a DBS sampling method for simultaneous measurement of vancomycin and creatinine, allowing for direct use in (outpatient) practice.
“Svá daufr orðinn, at hann mátti eigi heyra”: a critical exploration of depictions of deaf and non-speaking characters in medieval Icelandic literature
Authors: Hardonk Stefan C , Tirosh Yoav 
Published: January 2025

Abstract
This article revisits the polarities of oralist and cultural-linguistic approaches to deaf identities through the perspective of the medieval Icelandic sagas, a product of mainly the thirteenth to fifteenth centuries. Through a historically informed close reading of the Sagas of Early Icelanders corpus, 5 saga episodes were selected for further analysis with regard to the meaning of being deaf and/or non-speaking and intersections with other social categories like gender and class. This study suggests diverse ways in which being deaf and/or non-speaking was given meaning before the advent of oralism and the establishment of Deaf communities in Iceland, that is, as traits that lead to social exclusion as well as a part of the identities of individuals with considerable social status. An intersectional perspective shows male deaf characters exerting control over their lives and yielding political power, while deaf and non-speaking women are more portrayed as disenfranchised, regardless of their social class.
The neurorepellent SLIT2 inhibits LPS-induced proinflammatory signaling in macrophages
Authors: Skrtic Marko , Yusuf Bushra , Patel Sajedabanu , Reddy Emily C , Ting Kenneth K Y , Cybulsky Myron I , Freeman Spencer A , Robinson Lisa A 
Published: January 2025

Abstract
Macrophages are important mediators of immune responses with critical roles in the recognition and clearance of pathogens, as well as in the resolution of inflammation and wound healing. The neuronal guidance cue SLIT2 has been widely studied for its effects on immune cell functions, most notably directional cell migration. Recently, SLIT2 has been shown to directly enhance bacterial killing by macrophages, but the effects of SLIT2 on inflammatory activation of macrophages are less known. Using RNA sequencing analysis, quantitative polymerase chain reaction, and enzyme-linked immunosorbent assay, we determined that in murine bone marrow–derived macrophages challenged with the potent proinflammatory mediator lipopolysaccharide (LPS), exposure to the bioactive N-terminal fragment of SLIT2 (NSLIT2) suppressed production of proinflammatory cytokines interleukin (IL)-6 and IL-12 and concurrently increased the anti-inflammatory cytokine IL-10. We found that NSLIT2 inhibited LPS-induced MyD88- and TRIF-mediated signaling cascades and did not inhibit LPS-induced internalization of Toll-like receptor 4 (TLR4), but instead inhibited LPS-induced upregulation of macropinocytosis. Inhibition of macropinocytosis in macrophages attenuated LPS-induced production of proinflammatory IL-6 and IL-12 and concurrently enhanced anti-inflammatory IL-10. Taken together, our results indicate that SLIT2 can selectively modulate macrophage response to potent proinflammatory stimuli, such as LPS, by attenuating proinflammatory activation and simultaneously enhancing anti-inflammatory activity. Our results highlight the role of macropinocytosis in proinflammatory activation of macrophages exposed to LPS. Given that LPS-producing bacteria cause host illness through synergistic direct bacterial infection and excessive LPS-induced systemic inflammation, our work suggests a novel therapeutic role for SLIT2 in combatting the significant morbidity and mortality of patients with Gram-negative bacterial sepsis.