bluuebunny/crossref_metadata_2025_split huggingface dataset:
Intensity noise and modulation dynamics of an epitaxial mid-infrared interband cascade laser on silicon
Authors: Kim H. , Didier P. , Zaminga S. , Díaz-Thomas D. A. , Baranov A. N. , Rodriguez J. B. , Tournié E. , Knötig H. , Schwarz B. , Cerutti L. , Spitz O. , Grillot F. 
Published: October 2024

Interband cascade lasers typically have significantly lower threshold current and power consumption than quantum cascade lasers. They can also have advantages regarding costs and compactness with the photonic integration onto silicon substrates by epitaxial growth. This research introduces a novel examination of the relative intensity noise and the modulation dynamics of a silicon-based Fabry–Perot interband cascade laser emitting at 3.5 μm. The investigation delves into crucial parameters, such as relaxation oscillation frequency, differential gain, gain compression, and K-factor. The resonance patterns identified in relative intensity noise curves can provide essential insights for the thorough characterization of high-defect mid-infrared semiconductor structures intended for high-speed applications. Moreover, this study demonstrates the feasibility of reaching 10 Gbit/s free-space transmission using a silicon-based interband cascade laser in conjunction with an interband cascade infrared photodetector.
Ad-hoc specialized corpora in translation teaching
Authors: Aragrande Gaia 
Published: October 2024

Abstract
This paper shows how building a specialised corpus of heritage tourism texts (Heritage Tourism Corpus — HT Corpus) represents
the ideal trade-union between the need of developing lingua-cultural competence in a specialized language and training
the ability of translating tourist texts from Italian to English especially when it comes to domain-specific terminology and
cultural-specific items.
The HT Corpus comprises texts from anglophone heritage tourism websites (approx. 1 mln words). It was built by professors at
the University of Bologna as part of an MA course on English for Special Purposes and translation. This paper thus contains a progress
report relaying preliminary findings from the classroom. Within this MA course, the use of the HT Corpus has a twofold aim: (1) letting
students familiarise and investigate the ESP of tourism by perusing the HT Corpus mainly with frequency lists, collocations and keywords;
(2) aiding them in the translation of Italian heritage tourism source texts (STs) into English target texts (TTs).
The challenges of translation and interpretation in international projects
Authors: García Antonio Bueno 
Published: October 2024

Abstract
This work presents a practical perspective on the challenges of translation and interpretation in the context of an
international project. It uses the European Twinning project titled “Support to the Ministry of Higher Education and Scientific Research
for the strengthening of pedagogical competencies of teacher-researchers and the governance capacities of managers” (PAPERS) as a case
study. The unique situations that emerged during this project, from linguistic, cultural, social, political, economic, and even health
perspectives — arising from the impact of the 2019 coronavirus pandemic in its midway development — make it an intriguing subject for
observation within the domains of Translation, Interpretation, Education, and related areas concerning learning methods and the use of new
technologies.
Where Did It Come From? Where Will It Go?
Authors: Williamson Timothy 
Published: October 2024

Abstract
This chapter is an overview of both the origins of the knowledge-first approach in Knowledge and its Limits and some of the most promising directions in which it is now developing. The first half discusses knowledge-first epistemology in the tradition of Oxford realism, the author’s earlier work on indiscriminability, the relation between knowledge and action, externalism about content and externalism about attitudes, and assertability, and the origin of the phrase ‘knowledge first’. The second half discusses a revised treatment of the relation between knowledge and action, a knowledge-first approach to the psychology of mind reading, formal models of knowledge, and the nature of epistemic norms.
Śrīharṣa on the Indefinability of Knowledge Events
Authors: Das Nilanjan 
Published: October 2024

Abstract
In Sanskrit philosophy, epistemologists were preoccupied with the notion of pramā. A pramā, roughly, is a mental event of learning or knowledge acquisition—an experience or thought in undergoing which an agent learns or comes to know something. Call any such mental event a knowledge event. The twelfth-century Sanskrit philosopher Śrīharṣa argued that knowledge events are indefinable. Any satisfactory (and therefore non-circular) definition of knowledge events will have to include an anti-luck condition that doesn’t appeal back to the notion of learning or knowledge acquisition itself. But there is no such anti-luck condition. Śrīharṣa went on to claim that, given some other assumptions, the indefinability of knowledge events should give us reason to doubt that our ordinary attributions of knowledge events are reliably tracking any sui generis mental kind. This chapter reconstructs Śrīharṣa’s position and explains how it extends to contemporary knowledge-first approaches to epistemology.
How the Brain Makes Knowledge First
Authors: Gordon Robert M 
Published: October 2024

Abstract
To interpret the actions of others, it has been proposed that the brain harnesses its capacity for action planning to test hypothetical ways of generating another agent’s actions. This inverse use of action planning runs concurrently with planning one’s own actions and in the default case is constrained by the same inputs (agent-neutral coding) for self and other. The upshot is that factual input is passed along to the other agent as known facts. Thereby the brain makes knowledge the default epistemic attribution to others and can focus its resources on detecting exceptions. A postscript updates the author’s early work on factive emotions, which anticipated aspects of a knowledge-first epistemology.
Knowledge-First Philosophy of Science
Authors: Bird Alexander 
Published: October 2024

Abstract
This chapter examines the implications of knowledge‐first epistemology for philosophy of science, starting with the idea that belief aims at knowledge. Correspondingly, it claims that scientific belief and science itself aim at scientific knowledge. This implies an account of scientific progress as the accumulation of scientific knowledge. More importantly, it argues that we can also infer an account of scientific evidence: science aims at producing knowledge by making inferences from evidence; science can achieve this if and only if evidence is knowledge. This knowledge‐first equation, E=K, runs counter to empiricist conceptions of evidence and observation as tied to perception. The chapter notes that it provides an answer to problems raised by the Duhem–Quine thesis. It also enables a picture of science that allows that as hypotheses come to be known, the evidential basis of science can become ever more ‘theory‐laden’ in a benign way, allowing for further scientific progress.
Deep learning in template-free de novo biosynthetic pathway design of natural products
Authors: Xie Xueying , Gui Lin , Qiao Baixue , Wang Guohua , Huang Shan , Zhao Yuming , Sun Shanwen 
Published: October 2024

Abstract
Natural products (NPs) are indispensable in drug development, particularly in combating infections, cancer, and neurodegenerative diseases. However, their limited availability poses significant challenges. Template-free de novo biosynthetic pathway design provides a strategic solution for NP production, with deep learning standing out as a powerful tool in this domain. This review delves into state-of-the-art deep learning algorithms in NP biosynthesis pathway design. It provides an in-depth discussion of databases like Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome, and UniProt, which are essential for model training, along with chemical databases such as Reaxys, SciFinder, and PubChem for transfer learning to expand models’ understanding of the broader chemical space. It evaluates the potential and challenges of sequence-to-sequence and graph-to-graph translation models for accurate single-step prediction. Additionally, it discusses search algorithms for multistep prediction and deep learning algorithms for predicting enzyme function. The review also highlights the pivotal role of deep learning in improving catalytic efficiency through enzyme engineering, which is essential for enhancing NP production. Moreover, it examines the application of large language models in pathway design, enzyme discovery, and enzyme engineering. Finally, it addresses the challenges and prospects associated with template-free approaches, offering insights into potential advancements in NP biosynthesis pathway design.
DynProfiler: a Python package for comprehensive analysis and interpretation of signaling dynamics leveraged by deep learning techniques
Authors: Tsutsui Masato , Okada Mariko 
Published: October 2024

Abstract

Summary
Signaling dynamics encode important features and regulatory mechanisms of biological systems, and recent studies have reported the use of simulated signaling dynamics with mechanistic modeling as biomarkers for human diseases. Given the success of deep learning techniques, it is expected that they can extract informative patterns from simulation results more effectively than traditional approaches involving manual feature selection, which can be used for subsequent analyses, such as patient stratification and survival prediction. Here, we propose DynProfiler, which utilizes the entire signaling dynamics, including intermediate variables, as input and leverages deep learning techniques to extract informative features without requiring any labels. Furthermore, DynProfiler incorporates a modern explainable AI solution to provide quantitative time-dependent importance scores for each dynamics. Using simulated dynamics of patients with breast cancer as an example, we demonstrate DynProfiler’s ability to extract high-quality features that can predict mortality risk and identify important dynamics, highlighting upregulated phosphorylated GSK3β as a biomarker for poor prognosis. Overall, this tool can be useful for clinical application, as well as for elucidating biological system dynamics.

Availability and implementation
The DynProfiler Python library is available in GitHub at https://github.com/okadalabipr/DynProfiler.
BioPred: an R package for biomarkers analysis in precision medicine
Authors: Liu Zihuan , Sun Yan , Huang Xin 
Published: October 2024

Abstract

Summary
The R package BioPred offers a suite of tools for subgroup and biomarker analysis in precision medicine. Leveraging Extreme Gradient Boosting (XGBoost) along with propensity score weighting and A-learning methods, BioPred facilitates the optimization of individualized treatment rules to streamline subgroup identification. BioPred also enables the identification of predictive biomarkers and obtaining their importance rankings. Moreover, the package provides graphical plots tailored for biomarker analysis. This tool enables clinical researchers seeking to enhance their understanding of biomarkers and patient population in drug development.

Availability and implementation
The package is available at CRAN and https://github.com/deeplearner0731/BioPred.
CyCadas: accelerating interactive annotation and analysis of clustered cytometry data
Authors: Hunewald Oliver , Demczuk Agnieszka , Longworth Joseph , Ollert Markus 
Published: October 2024

Abstract

Motivation
Single cell profiling by cytometry has emerged as a key technology in biology, immunology and clinical-translational medicine. The correct annotation, which refers to the identification of clusters as specific cell populations based on their marker expression, of clustered high-dimensional cytometry data, is a critical step of the analysis. Its accuracy determines the correct interpretation of the biological data. Despite the progress in various clustering algorithms, the annotation of clustered data still remains a manual, time consuming and error-prone task. We developed a user-friendly cluster annotation and differential abundance detection tool that can be applied on data generated with Self Organizing Map clustering algorithms, thus simplifying the annotation process of datasets that consist of hundreds or thousands of clusters.

Results
We present Cytometry Cluster Annotation and Differential Abundance Suite (CyCadas), a semi-automated software tool that facilitates cluster annotation in cytometry data by offering both visual and computational guidance. CyCadas addresses the critical need for efficient and accurate annotation of high-resolution clustered cytometry data, significantly reducing the time needed to perform the analysis compared to both manual gating approaches and manual annotation of clustered data. The tool features a user-friendly interface, visual tools enabling data exploration and automated threshold estimation to separate negative and positive marker expression. It facilitates the definition and annotation of cell phenotypes among multiple clusters in a tree-based data structure. Finally, it calculates the abundance of various cell populations across the conditions with statistical interpretation. It is an ideal resource for researchers aiming to streamline their cytometry workflow.

Availability and implementation
CyCadas is available as open source at: https://github.com/DII-LIH-Luxembourg/cycadas.
Individualized cortical gyrification in neonates with congenital heart disease
Authors: Cromb Daniel , Wilson Siân , Bonthrone Alexandra F , Chew Andrew , Kelly Christopher , Kumar Manu , Cawley Paul , Dimitrova Ralica , Arichi Tomoki , Tournier J Donald , Pushparajah Kuberan , Simpson John , Rutherford Mary , Hajnal Joseph V , Edwards A David , Nosarti Chiara , O’Muircheartaigh Jonathan , Counsell Serena J 
Published: October 2024

Abstract
Congenital heart disease is associated with impaired early brain development and adverse neurodevelopmental outcomes. This study investigated how individualized measures of preoperative cortical gyrification index differ in 142 infants with congenital heart disease, using a normative modelling approach with reference data from 320 typically developing infants. Gyrification index Z-scores for the whole brain and six major cortical areas were generated using two different normative models: one accounting for post-menstrual age at scan, post-natal age at scan and sex, and another additionally accounting for supratentorial brain volume. These Z-scores were compared between congenital heart disease and control groups to test the hypothesis that cortical folding in infants with congenital heart disease deviates from the normal developmental trajectory. The relationships between whole-brain gyrification index Z-scores from the two normative models and both cerebral oxygen delivery and neurodevelopmental outcomes were also investigated. Global and regional brain gyrification was significantly reduced in neonates with congenital heart disease, but not when supratentorial brain volume was accounted for. This finding suggests that whilst cortical folding is reduced in congenital heart disease, it is primarily driven by a reduction in brain size. There was a significant positive correlation between cerebral oxygen delivery and whole-brain gyrification index Z-scores in congenital heart disease, but not when supratentorial brain volume was accounted for. Cerebral oxygen delivery is therefore likely to play a more important role in the biological processes underlying volumetric brain growth than cortical folding. No significant associations between whole-brain gyrification index Z-scores and motor/cognitive outcomes or autism traits were identified in the 70 infants with congenital heart disease who underwent neurodevelopmental assessment at 22-months. Our results suggest that chronic in utero and early post-natal hypoxia in congenital heart disease is associated with reductions in cortical folding that are proportional to reductions in supratentorial brain volume.
Fibroblast-specific TGF-β signaling mediates cardiac dysfunction, fibrosis, and hypertrophy in obese diabetic mice
Authors: Tuleta Izabela , Hanna Anis , Humeres Claudio , Aguilan Jennifer T , Sidoli Simone , Zhu Fenglan , Frangogiannis Nikolaos G 
Published: October 2024

Abstract

Aims
Transforming growth factor (TGF)-β is up-regulated in the diabetic myocardium and may mediate fibroblast activation. We aimed at examining the role of TGF-β-induced fibroblast activation in the pathogenesis of diabetic cardiomyopathy.

Methods and results
We generated lean and obese db/db mice with fibroblast-specific loss of TbR2, the Type 2 receptor-mediating signaling through all three TGF-β isoforms, and mice with fibroblast-specific Smad3 disruption. Systolic and diastolic function, myocardial fibrosis, and hypertrophy were assessed. Transcriptomic studies and in vitro experiments were used to dissect mechanisms of fibroblast activation. Fibroblast-specific TbR2 loss attenuated systolic and diastolic dysfunction in db/db mice. The protective effects of fibroblast TbR2 loss in db/db mice were associated with attenuated fibrosis and reduced cardiomyocyte hypertrophy, suggesting that in addition to their role in fibrous tissue deposition, TGF-β-stimulated fibroblasts may also exert paracrine actions on cardiomyocytes. Fibroblast-specific Smad3 loss phenocopied the protective effects of fibroblast TbR2 loss in db/db mice. Db/db fibroblasts had increased expression of genes associated with oxidative response (such as Fmo2, encoding flavin-containing monooxygenase 2), matricellular genes (such as Thbs4 and Fbln2), and Lox (encoding lysyl oxidase). Ingenuity pathway analysis (IPA) predicted that neurohumoral mediators, cytokines, and growth factors (such as AGT, TGFB1, and TNF) may serve as important upstream regulators of the transcriptomic profile of diabetic mouse fibroblasts. IPA of scRNA-seq data identified TGFB1, p53, MYC, PDGF-BB, EGFR, and WNT3A/CTNNB1 as important upstream regulators underlying fibroblast activation in db/db hearts. Comparison of the transcriptome of fibroblasts from db/db mice with fibroblast-specific Smad3 loss and db/db Smad3 fl/fl controls identified Thbs4 [encoding thrombospondin-4 (TSP-4), a marker of activated fibroblasts] as a candidate diabetes-induced fibrogenic mediator. However, in vitro experiments showed no significant activating effects of matricellular or intracellular TSP-4 on cardiac fibroblasts.

Conclusion
Fibroblast-specific TGF-β/Smad3 signaling mediates ventricular fibrosis, hypertrophy, and dysfunction in Type 2 diabetes.
Familial risk of myocardial infarction with non-obstructive and obstructive coronary arteries: a nation-wide cohort study
Authors: Hakansson Felicia H K , Svensson Per , Pettersson Hans J , Ehrenborg Ewa , Spaak Jonas , Nordenskjold Anna M , Eggers Kai M , Tornvall Per 
Published: October 2024

Abstract

Aims
The familial risk among patients with myocardial infarction with non-obstructive coronary arteries (MINOCA) is unknown. Previous studies of family history in myocardial infarction (MI) have not made a distinction between MINOCA and MI due to coronary artery disease (MI-CAD), based on angiographic findings. We therefore sought to investigate familial risk of MI without and with obstructive coronary arteries.

Methods and results
Register-based cohort study with a total of 15 462 MINOCA cases, 204 424 MI-CAD cases, 38 220 control subjects without MI, and with non-obstructive coronary arteries. First-degree relatives were identified in 1995–2020. Cox proportional hazard regression models were used to compare familial risk in MINOCA and MI-CAD with control subjects. During a mean follow-up of 8.1 ± 4.2 years, MINOCA occurred in 1.0% of first-degree relatives with MINOCA whereas MI-CAD occurred in 9.7% of first-degree relatives of MINOCA. The age- and sex-adjusted hazard ratio (HR) for a MINOCA relative experiencing MINOCA and MI-CAD, compared to control subjects, was 0.99 [95% confidence interval (CI) 0.80–1.23] and 1.10 (95% CI 1.03–1.18), respectively. During a mean follow-up of 8.5 ± 4.8 years, MI-CAD occurred in 12.2% of first-degree relatives with MI-CAD with age- and sex-adjusted HR 1.43 (95% CI 1.37–1.49).

Conclusion
No increased familial risk of MINOCA was observed for MINOCA patients whereas there was an increased familial risk for MI-CAD when compared to control subjects. These results may indicate that genetic factors and shared environmental factors within a family leading to CAD are important also for MINOCA, thus MI-CAD and MINOCA could share underlying mechanisms.
Microbiome Geographic Population Structure (mGPS) Detects Fine-Scale Geography
Authors: Zhang Yali , McCarthy Leo , Ruff Emil , Elhaik Eran 
Published: October 2024

Abstract
Over the past decade, sequencing data generated by large microbiome projects showed that taxa exhibit patchy geographical distribution, raising questions about the geospatial dynamics that shape natural microbiomes and the spread of antimicrobial resistance genes. Answering these questions requires distinguishing between local and nonlocal microorganisms and identifying the source sites for the latter. Predicting the source sites and migration routes of microbiota has been envisioned for decades but was hampered by the lack of data, tools, and understanding of the processes governing biodiversity. State-of-the-art biogeographical tools suffer from low resolution and cannot predict biogeographical patterns at a scale relevant to ecological, medical, or epidemiological applications. Analyzing urban, soil, and marine microorganisms, we found that some taxa exhibit regional-specific composition and abundance, suggesting they can be used as biogeographical biomarkers. We developed the microbiome geographic population structure, a machine learning–based tool that utilizes microbial relative sequence abundances to yield a fine-scale source site for microorganisms. Microbiome geographic population structure predicted the source city for 92% of the samples and the within-city source for 82% of the samples, though they were often only a few hundred meters apart. Microbiome geographic population structure also predicted soil and marine sampling sites for 86% and 74% of the samples, respectively. We demonstrated that microbiome geographic population structure differentiated local from nonlocal microorganisms and used it to trace the global spread of antimicrobial resistance genes. Microbiome geographic population structure's ability to localize samples to their water body, country, city, and transit stations opens new possibilities in tracing microbiomes and has applications in forensics, medicine, and epidemiology.