bluuebunny/crossref_metadata_2025_split huggingface dataset:
3-1-3 Weight averaging technique-based performance evaluation of deep neural networks for Alzheimer’s disease detection using structural MRI
Authors: Gautam Priyanka , Singh Manjeet 
Published: August 2024

Abstract
Alzheimer’s disease (AD) is a progressive neurological disorder. It is identified by the gradual shrinkage of the brain and the loss of brain cells. This leads to cognitive decline and impaired social functioning, making it a major contributor to dementia. While there are no treatments to reverse AD’s progression, spotting the disease’s onset can have a significant impact in the medical field. Deep learning (DL) has revolutionized medical image classification by automating feature engineering, removing the requirement for human experts in feature extraction. DL-based solutions are highly accurate but demand a lot of training data, which poses a common challenge. Transfer learning (TL) has gained attention for its knack for handling limited data and expediting model training. This study uses TL to classify AD using T1-weighted 3D Magnetic Resonance Imaging (MRI) from the Alzheimer’s Disease Neuroimaging (ADNI) database. Four modified pre-trained deep neural networks (DNN), VGG16, MobileNet, DenseNet121, and NASNetMobile, are trained and evaluated on the ADNI dataset. The 3-1-3 weight averaging technique and fine-tuning improve the performance of the classification models. The evaluated accuracies for AD classification are VGG16: 98.75%; MobileNet: 97.5%; DenseNet: 97.5%; and NASNetMobile: 96.25%. The receiver operating characteristic (ROC), precision–recall (PR), and Kolmogorov-Smirnov (KS) statistic plots validate the effectiveness of the modified pre-trained model. Modified VGG16 excels with area under the curve (AUC) values of 0.99 for ROC and 0.998 for PR curves. The proposed approach shows effective AD classification by achieving high accuracy using the 3-1-3 weight averaging technique and fine-tuning.
Introduction
Authors: Lewis Hannah 
Published: August 2024

Abstract
The introduction presents La La Land and its place in contemporary culture. It describes the debacle at the 2017 Oscars, when La La Land was mistakenly announced as the Best Picture winner, and suggests that the awkward mix-up was in many ways symbolic of the film’s broader cultural reception since its premiere. The film looked backward in some ways and forward in others, revealing much about the film musical genre’s enduring and evolving place in American culture. The introduction additionally presents the overall scope and argument of the book, briefly describes the plot and musical numbers, and outlines the major themes in the chapters that will follow.
Reception
Authors: Lewis Hannah 
Published: August 2024

Abstract
Chapter 5 examines La La Land’s reception, from its initial film festival run in the summer of 2016 through the Academy Awards ceremony in February 2017. Its evolving reception was shaped by several factors, including a shift in national mood in response to current events right around its premiere, as well as the simultaneous critical success of Moonlight, a film that symbolized a very different approach to diversity and representation. The reception of La La Land captured the essence of much broader cultural debates that were taking place around Hollywood, representation, and the kinds of stories and experiences that were being valued and presented onscreen.
Orff’s Narrative-Dramatic Cycle
Authors: Oade Stephanie 
Published: August 2024

Abstract
Chapter 8 considers Carl Orff’s Catulli Carmina from musical and dramatic perspectives. Although Orff’s cycle offered the suggestion of narrative, there were various challenges to overcome in order to create a sense of linear progression through the work. To this end Orff employed dramatic characters, speech, narrative voices, physical action, and stagecraft, in an attempt to move the lyric poems towards a dramatic presentation. The chapter also explores how Orff created a frame construct to objectify the poetry and impose Orffian symbolism on the original text. Although these dramatic adaptations create tension with the lyricism of the poems, the work offers an opportunity to explore the complex overlaps and incompatibilities between and within the genres of drama and lyric.
A Lyric Figure: Ariadne
Authors: Oade Stephanie 
Published: August 2024

Abstract
This chapter focuses on the figure of Ariadne who, in both Catullus 64 and Strauss–Hofmannsthal’s Ariadne auf Naxos, is constructed and presented as a lyric figure in her own right. This lyricism is considered from a variety of angles, including visual (in her isolation and liminality), stylistic (das Gleitende), thematic (shared concerns for marriage, sex, and death), and tonal (emotional subjectivity in her lament). Socio-cultural readings are introduced in order to interpret the two Ariadnes as models of femininity, mediated by their male creators and representative of their respective times. Her characterization raises discussion of her agency and transformative potential, concepts that are central to her interpretation in both works.
Strauss’s Musical Drama
Authors: Oade Stephanie 
Published: August 2024

Abstract
The final analysis chapter concentrates on the music of Richard Strauss. It offers a reading and interpretation of the music, contextualized by a wider discussion of Strauss’s musical style, including his orchestration, harmonic language, as well as his use of Wagnerian leitmotifs and historical pastiche. Strauss was perturbed by the essential lyricism of Hofmannsthal’s libretto and worked to counter this through the use of increasingly motivic language, thus creating the effect of musical narrative for a dramatic and progressive outline that leads to the ultimate transformation of both Ariadne and Bacchus. Study of Strauss’s musical fabric throws into relief many of Hofmannsthal’s lyric techniques and symbolic aims, whilst adding new dimensions to the interpretation of Catullus’s original poem.
Conclusion
Authors: Oade Stephanie 
Published: August 2024

Abstract
The volume closes by relating the conclusions of each case study to the wider conception of the lyric genre. It focuses on the limitations and advantages of seeking to establish a set of lyric characteristics, and the possibility of reading such lyric qualities across media during and through the process of reception. It also unravels the potential for lyric aspects to be embedded in narrative or dramatic forms, and questions the implications of this for the theorization of lyric and for the reading of Catullus. It establishes a framework for working with these ideas and sets the conclusions of the volume in a wider bibliographic context.
The Context: Ideas of Lyric
Authors: Oade Stephanie 
Published: August 2024

Abstract
This introduction to Part I outlines the context around the ideas of lyric that prevail in twenty-first-century scholarship. Since Part I seeks to establish a sense of the lyric tradition within which Catullus was working, this introduction begins with a discussion of some of the difficulties that surround the generic categorization of Catullus’s poetry and an overview of some of the influential ideas about Catullan lyricism from twentieth-century scholars. Two different approaches to generic categorization in lyric theory are laid out—the writer-led and the reader-led approaches—and these are used to establish methodological boundaries for the chapters that follow.
The Context: The Presence of Catullus 64 in Strauss–Hofmannsthal’s Ariadne auf Naxos
Authors: Oade Stephanie 
Published: August 2024

Abstract
Part IV: Lyric and Drama explores the opera Ariadne auf Naxos by Richard Strauss and Hugo von Hofmannsthal in conjunction with Catullus 64. The opera’s hitherto underestimated debt to Catullus sheds new light on its many generic and interpretative contradictions and helps to contextualize its experimental nature. Indeed, both Catullus 64 and Ariadne auf Naxos display generic hybridity, meaning that a joint study of the poem and opera offers the opportunity to explore the ways in which lyricism can be embedded within other genres, such as the narrative epyllion in hexameters and the dramatic opera form. The context section that opens Part IV outlines the relationship between the Strauss–Hofmannsthal opera and Catullus’s epyllion, poem 64. It focuses particularly on the ekphrasis and frame structures, as well as the parallel Ariadne figures, going on to contextualize the interpretation of Ariadne in the context of her wider reception in music. After an overview of both poem and opera, the respective educations of Strauss and Hofmannsthal are considered, as well as their ongoing engagement with the classics.
Harnessing large language models’ zero-shot and few-shot learning capabilities for regulatory research
Authors: Meshkin Hamed , Zirkle Joel , Arabidarrehdor Ghazal , Chaturbedi Anik , Chakravartula Shilpa , Mann John , Thrasher Bradlee , Li Zhihua 
Published: August 2024

Abstract
Large language models (LLMs) are sophisticated AI-driven models trained on vast sources of natural language data. They are adept at generating responses that closely mimic human conversational patterns. One of the most notable examples is OpenAI's ChatGPT, which has been extensively used across diverse sectors. Despite their flexibility, a significant challenge arises as most users must transmit their data to the servers of companies operating these models. Utilizing ChatGPT or similar models online may inadvertently expose sensitive information to the risk of data breaches. Therefore, implementing LLMs that are open source and smaller in scale within a secure local network becomes a crucial step for organizations where ensuring data privacy and protection has the highest priority, such as regulatory agencies. As a feasibility evaluation, we implemented a series of open-source LLMs within a regulatory agency’s local network and assessed their performance on specific tasks involving extracting relevant clinical pharmacology information from regulatory drug labels. Our research shows that some models work well in the context of few- or zero-shot learning, achieving performance comparable, or even better than, neural network models that needed thousands of training samples. One of the models was selected to address a real-world issue of finding intrinsic factors that affect drugs' clinical exposure without any training or fine-tuning. In a dataset of over 700 000 sentences, the model showed a 78.5% accuracy rate. Our work pointed to the possibility of implementing open-source LLMs within a secure local network and using these models to perform various natural language processing tasks when large numbers of training examples are unavailable.
HHOMR: a hybrid high-order moment residual model for miRNA-disease association prediction
Authors: Li Zhengwei , Wan Lipeng , Wang Lei , Wang Wenjing , Nie Ru 
Published: August 2024

Abstract
Numerous studies have demonstrated that microRNAs (miRNAs) are critically important for the prediction, diagnosis, and characterization of diseases. However, identifying miRNA–disease associations through traditional biological experiments is both costly and time-consuming. To further explore these associations, we proposed a model based on hybrid high-order moments combined with element-level attention mechanisms (HHOMR). This model innovatively fused hybrid higher-order statistical information along with structural and community information. Specifically, we first constructed a heterogeneous graph based on existing associations between miRNAs and diseases. HHOMR employs a structural fusion layer to capture structure-level embeddings and leverages a hybrid high-order moments encoder layer to enhance features. Element-level attention mechanisms are then used to adaptively integrate the features of these hybrid moments. Finally, a multi-layer perceptron is utilized to calculate the association scores between miRNAs and diseases. Through five-fold cross-validation on HMDD v2.0, we achieved a mean AUC of 93.28%. Compared with four state-of-the-art models, HHOMR exhibited superior performance. Additionally, case studies on three diseases—esophageal neoplasms, lymphoma, and prostate neoplasms—were conducted. Among the top 50 miRNAs with high disease association scores, 46, 47, and 45 associated with these diseases were confirmed by the dbDEMC and miR2Disease databases, respectively. Our results demonstrate that HHOMR not only outperforms existing models but also shows significant potential in predicting miRNA–disease associations.
HTINet2: herb–target prediction via knowledge graph embedding and residual-like graph neural network
Authors: Duan Pengbo , Yang Kuo , Su Xin , Fan Shuyue , Dong Xin , Zhang Fenghui , Li Xianan , Xing Xiaoyan , Zhu Qiang , Yu Jian , Zhou Xuezhong 
Published: August 2024

Abstract
Target identification is one of the crucial tasks in drug research and development, as it aids in uncovering the action mechanism of herbs/drugs and discovering new therapeutic targets. Although multiple algorithms of herb target prediction have been proposed, due to the incompleteness of clinical knowledge and the limitation of unsupervised models, accurate identification for herb targets still faces huge challenges of data and models. To address this, we proposed a deep learning-based target prediction framework termed HTINet2, which designed three key modules, namely, traditional Chinese medicine (TCM) and clinical knowledge graph embedding, residual graph representation learning, and supervised target prediction. In the first module, we constructed a large-scale knowledge graph that covers the TCM properties and clinical treatment knowledge of herbs, and designed a component of deep knowledge embedding to learn the deep knowledge embedding of herbs and targets. In the remaining two modules, we designed a residual-like graph convolution network to capture the deep interactions among herbs and targets, and a Bayesian personalized ranking loss to conduct supervised training and target prediction. Finally, we designed comprehensive experiments, of which comparison with baselines indicated the excellent performance of HTINet2 (HR@10 increased by 122.7% and NDCG@10 by 35.7%), ablation experiments illustrated the positive effect of our designed modules of HTINet2, and case study demonstrated the reliability of the predicted targets of Artemisia annua and Coptis chinensis based on the knowledge base, literature, and molecular docking.
PUNCH CD3-OLS: A Phase 3 Prospective Observational Cohort Study to Evaluate the Safety and Efficacy of Fecal Microbiota, Live-jslm (REBYOTA) in Adults With Recurrent Clostridioides difficile Infection
Authors: Feuerstadt Paul , Chopra Teena , Knapple Whitfield , Van Hise Nicholas W , Dubberke Erik R , Baggott Brian , Guthmueller Beth , Bancke Lindy , Gamborg Michael , Steiner Theodore S , Van Handel Daniel , Khanna Sahil 
Published: August 2024

Abstract

Background
The aim of this study was to evaluate the safety and efficacy of fecal microbiota, live-jslm (RBL; REBYOTA)—the first single-dose, broad consortia microbiota-based live biotherapeutic approved by the US Food and Drug Administration for preventing recurrent Clostridioides difficile infection (rCDI) in adults following standard-of-care (SOC) antibiotic treatment.

Methods
PUNCH CD3-OLS was a prospective, phase 3, open-label study, conducted across the US and Canada. Participants were aged ≥18 years with documented rCDI and confirmed use of SOC antibiotics. Participants with comorbidities including inflammatory bowel disease and mild-to-moderate immunocompromising conditions could be enrolled. A single dose of RBL was rectally administered within 24–72 hours of antibiotic completion. The primary endpoint was the number of participants with RBL- or administration-related treatment-emergent adverse events (TEAEs). Secondary endpoints included treatment success and sustained clinical response, at 8 weeks and 6 months after RBL administration, respectively.

Results
Overall, 793 participants were enrolled, of whom 697 received RBL. TEAEs through 8 weeks after administration were reported by 47.3% of participants; most events were mild or moderate gastrointestinal disorders. Serious TEAEs were reported by 3.9% of participants. The treatment success rate at 8 weeks was 73.8%; in participants who achieved treatment success, the sustained clinical response rate at 6 months was 91.0%. Safety and efficacy rates were similar across demographic and baseline characteristic subgroups.

Conclusions
RBL was safe and efficacious in participants with rCDI and common comorbidities. This is the largest microbiota-based live biotherapeutic study to date, and findings support use of RBL to prevent rCDI in a broad patient population.

Clinical Trials Registration
NCT03931941.
Non-dilated obstructive nephropathy
Authors: Feliciangeli Valeria , Noce Annalisa , Montalto Giulia , Germani Stefano , Miano Roberto , Asimakopoulos Anastasios D 
Published: August 2024

ABSTRACT
Obstructive nephropathy (ON) is a common and reversible cause of post-renal acute kidney injury (AKI) and may be caused by a variety of conditions. It occurs when both the upper urinary tracts are obstructed, or when one tract is obstructed in patients with a solitary kidney. ON is suspected whenever there is evidence of hydronephrosis at imaging. However, not all patients with obstruction develop hydronephrosis and significant obstruction can be present in the absence of hydronephrosis. This syndrome is called non-dilated obstructive uropathy (NDOU). It accounts for about 5% of cases of urinary obstruction and the diagnosis can be challenging. The current paper provides an overview of the literature aiming to identify the main causes of NDOU and its clinical presentation, in order to clarify when to suspect it among AKI cases. A narrative review was performed due to the overall low quality of the available evidence. Only patients with post-renal AKI and a non-dilated or minimal dilation of the intrarenal collecting system were included. As evidenced by our review, NDOU is most prevalent in the fifth and sixth decades of life and affects mainly the male gender. On hospital admission serum creatinine levels are usually very high. Among the most common clinical presentations are oliguria/anuria, abdominal pain, signs of retention such as oedema or pleural effusion, and nausea/vomiting. About three out of four cases of NDOU are due to an ab-extrinsic compression of the ureters caused by retroperitoneal fibrosis or malignant disease. An effective and minimally invasive urinary diversion is obtained with ureteric stenting or a percutaneous nephrostomy. A correct diagnosis of NDOU may be challenging but it is of paramount importance as it can lead to a prompt management with a potential complete resolution of both obstruction and acute renal failure.
Cardiac adverse drug reactions to COVID-19 vaccines. A cross-sectional study based on the Europe-wide data
Authors: Nazar Wojciech , Romantowski Jan , Niedoszytko Marek , Daniłowicz-Szymanowicz Ludmiła 
Published: August 2024

Abstract

Aims
We aimed to analyse serious cardiac adverse drug reactions to COVID-19 vaccines from the Europe-wide EudraVigilance database.

Methods and results
In this retrospective, cross-sectional study, the EudraVigilance database was searched to identify suspected serious cardiac post-vaccination adverse drug reactions to COVID-19 vaccines. This data was coupled with the number of total vaccine doses administered in the European Economic Area for Comirnaty (Pfizer BioNTech), Spikevax (Moderna), Vaxzevria (AstraZeneca), Jcovden (Janssen), Nuvaxovid (Novavax), products, available from the European Centre for Disease Prevention and Control ‘Vaccine Tracker’ database. The analysis included 772 228 309 administered doses of eligible vaccines from the ‘Vaccine Tracker’ database and 86 051 eligible records of cardiac adverse drug reactions from the EudraVigilance database. The frequency of most of the investigated adverse drug reactions was very rare (&lt;1/10 000 i.e. &lt;100/1 000 000 doses). The lowest risk of any serious cardiac adverse drug reactions was noticed for vaccination with Comirnaty (135.5 per million doses), while Spikevax, Jcovden, Vaxzevria, and Nuvaxovid were characterized by higher risk (respectively, 140.9, 194.8, 313.6, and 1065.2 per million doses). The most common complications of vaccinations included syncope, arrhythmia, tachycardia, palpitations, angina pectoris, hypertension, myocarditis, thrombosis, and pulmonary embolism.

Conclusion
The risk of serious cardiac adverse drug reactions to COVID-19 vaccines is low and the benefit of active immunization against that disease seems to outweigh the potential risk of serious post-vaccination cardiac adverse drug reactions.
A case of large aneurysms of the coronary arteries and abdominal aorta in an infant
Authors: Sasikumar Deepa , Bhattacharya Deepanjan , Ayyappan Anoop , Shivanesan Pitchai 
Published: August 2024

Abstract
Aneurysms of the coronary arteries and abdominal aorta are extremely rare in infancy. Due to the rarity of the anomaly, there are no existing guidelines on management of these aneurysms. We describe the challenges in diagnosis, evaluation and management of an infantile Marfan with this rare presentation.
Impact of effective refractory period personalization on arrhythmia vulnerability in patient-specific atrial computer models
Authors: Martínez Díaz Patricia , Dasí Albert , Goetz Christian , Unger Laura A , Haas Annika , Luik Armin , Rodríguez Blanca , Dössel Olaf , Loewe Axel 
Published: August 2024

Abstract

Aims
The effective refractory period (ERP) is one of the main electrophysiological properties governing arrhythmia, yet ERP personalization is rarely performed when creating patient-specific computer models of the atria to inform clinical decision-making. This study evaluates the impact of integrating clinical ERP measurements into personalized in silico models on arrhythmia vulnerability.

Methods and results
Clinical ERP measurements were obtained in seven patients from multiple locations in the atria. Atrial geometries from the electroanatomical mapping system were used to generate personalized anatomical atrial models. The Courtemanche M. et al. cellular model was adjusted to reproduce patient-specific ERP. Four modeling approaches were compared: homogeneous (A), heterogeneous (B), regional (C), and continuous (D) ERP distributions. Non-personalized approaches (A and B) were based on literature data, while personalized approaches (C and D) were based on patient measurements. Modeling effects were assessed on arrhythmia vulnerability and tachycardia cycle length, with sensitivity analysis on ERP measurement uncertainty. Mean vulnerability was 3.4 ± 4.0%, 7.7 ± 3.4%, 9.0 ± 5.1%, and 7.0 ± 3.6% for scenarios A–D, respectively. Mean tachycardia cycle length was 167.1 ± 12.6 ms, 158.4 ± 27.5 ms, 265.2 ± 39.9 ms, and 285.9 ± 77.3 ms for scenarios A–D, respectively. Incorporating perturbations to the measured ERP in the range of 2, 5, 10, 20, and 50 ms changed the vulnerability of the model to 5.8 ± 2.7%, 6.1 ± 3.5%, 6.9 ± 3.7%, 5.2 ± 3.5%, and 9.7 ± 10.0%, respectively.

Conclusion
Increased ERP dispersion had a greater effect on re-entry dynamics than on vulnerability. Inducibility was higher in personalized scenarios compared with scenarios with uniformly reduced ERP; however, this effect was reversed when incorporating fibrosis informed by low-voltage areas. Effective refractory period measurement uncertainty up to 20 ms slightly influenced vulnerability. Electrophysiological personalization of atrial in silico models appears essential and requires confirmation in larger cohorts.