bluuebunny/crossref_metadata_2025_split huggingface dataset:
Pediatric Electrocardiogram-Based Deep Learning to Predict Secundum Atrial Septal Defects
Authors: Mayourian Joshua , Geggel Robert , La Cava William G. , Ghelani Sunil J. , Triedman John K. 
Published: July 2024

AbstractSecundum atrial septal defect (ASD2) detection is often delayed, with the potential for late diagnosis complications. Recent work demonstrated artificial intelligence-enhanced ECG analysis shows promise to detect ASD2 in adults. However, its application to pediatric populations remains underexplored. In this study, we trained a convolutional neural network (AI-pECG) on paired ECG–echocardiograms (≤ 2 days apart) to detect ASD2 from patients ≤ 18 years old without major congenital heart disease. Model performance was evaluated on the first ECG–echocardiogram pair per patient for Boston Children’s Hospital internal testing and emergency department cohorts using area under the receiver operating (AUROC) and precision-recall (AUPRC) curves. The training cohort comprised of 92,377 ECG–echocardiogram pairs (46,261 patients; median age 8.2 years) with an ASD2 prevalence of 6.7%. Test groups included internal testing (12,631 patients; median age 7.4 years; 6.9% prevalence) and emergency department (2,830 patients; median age 7.5 years; 4.9% prevalence) cohorts. Model performance was higher in the internal test (AUROC 0.84, AUPRC 0.46) cohort than the emergency department cohort (AUROC 0.80, AUPRC 0.30). In both cohorts, AI-pECG outperformed ECG findings of incomplete right bundle branch block. Model explainability analyses suggest high-risk limb lead features include greater amplitude P waves (suggestive of right atrial enlargement) and V1 RSR’ (suggestive of RBBB). Our findings demonstrate the promise of AI-pECG to inexpensively screen and/or detect ASD2 in pediatric patients. Future multicenter validation and prospective trials to inform clinical decision making are warranted.
Recurrent double-conditional factor model
Authors: Fieberg Christian , Liedtke Gerrit , Poddig Thorsten 
Published: July 2024

AbstractIn economic applications, the behavior of objects (e.g., individuals, firms, or households) is often modeled as a function of microeconomic and/or macroeconomic conditions. While macroeconomic conditions are common to all objects and change only over time, microeconomic conditions are object-specific and thus vary both among objects and through time. The simultaneous modeling of microeconomic and macroeconomic conditions has proven to be extremely difficult for these applications due to the mismatch of dimensions, potential interactions, and the high number of parameters to estimate. By marrying recurrent neural networks with conditional factor models, we propose a new white-box machine learning method, the recurrent double-conditional factor model (RDCFM), which allows for the modeling of the simultaneous and combined influence of micro- and macroeconomic conditions while being parsimoniously parameterized. Due to the low degree of parameterization, the RDCFM generalizes well and estimation remains feasible even if the time-series and the cross-section are large. We demonstrate the suitability of our method using an application from the financial economics literature.
Generative adversarial networks for handwriting image generation: a review
Authors: Elanwar Randa , Betke Margrit 
Published: July 2024

AbstractHandwriting synthesis, the task of automatically generating realistic images of handwritten text, has gained increasing attention in recent years, both as a challenge in itself, as well as a task that supports handwriting recognition research. The latter task is to synthesize large image datasets that can then be used to train deep learning models to recognize handwritten text without the need for human-provided annotations. While early attempts at developing handwriting generators yielded limited results [1], more recent works involving generative models of deep neural network architectures have been shown able to produce realistic imitations of human handwriting [2–19]. In this review, we focus on one of the most prevalent and successful architectures in the field of handwriting synthesis, the generative adversarial network (GAN). We describe the capabilities, architecture specifics, and performance of the GAN-based models that have been introduced to the literature since 2019 [2–14]. These models can generate random handwriting styles, imitate reference styles, and produce realistic images of arbitrary text that was not in the training lexicon. The generated images have been shown to contribute to improving handwriting recognition results when augmenting the training samples of recognition models with synthetic images. The synthetic images were often hard to expose as non-real, even by human examiners, but also could be implausible or style-limited. The review includes a discussion of the characteristics of the GAN architecture in comparison with other paradigms in the image-generation domain and highlights the remaining challenges for handwriting synthesis.
Pioneers of cortical cytoarchitectonics: the forgotten contribution of Herbert Major
Authors: Larner Andrew J. , Triarhou Lazaros C. 
Published: July 2024

AbstractThe study of cortical cytoarchitectonics and the histology of the human cerebral cortex was pursued by many investigators in the second half of the nineteenth century, such as Jacob Lockhart Clarke, Theodor Meynert, and Vladimir Betz. Another of these pioneers, whose name has largely been lost to posterity, is considered here: Herbert Coddington Major (1850–1921). Working at the West Riding Asylum in Wakefield, United Kingdom, Major’s thesis of 1875 described and illustrated six-layered cortical structure in both non-human primates and man, as well as “giant nerve cells” which corresponded to those cells previously described, but not illustrated, by Betz. Further journal publications by Major in 1876 and 1877 confirmed his finding of six cortical strata. However, Major’s work was almost entirely neglected by his contemporaries, including his colleague and sometime pupil at the West Riding Asylum, William Bevan-Lewis (1847–1929), who later (1878) reported the presence of both pentalaminar and hexalaminar cortices. Bevan-Lewis’s work was also later credited with the first illustration of Betz cells.
Expecting politeness: perceptions of voice assistant politeness
Authors: Duffau Elise , Fox Tree Jean E. 
Published: July 2024

AbstractWe examined how politeness perception can change when used by a human or voice assistant in different contexts. We conducted two norming studies and two experiments. In the norming studies, we assessed the levels of positive politeness (cooperation) and negative politeness (respecting autonomy) conveyed by a range of politeness strategies across task (Norming Study 1) and social (Norming Study 2) request types. In the experiments, we tested the effect of request type and imposition level on the perception of written requests (Experiment 1) and requests spoken by a voice assistant (Experiment 2). We found that the perception of politeness strategies varied by request type. Positive politeness strategies were rated as very polite with task requests. In contrast, both positive and negative politeness strategies were rated as very polite with social requests. We also found that people expect agents to respect their autonomy more than they expect them to cooperate. Detailed studies of how request context interacts with politeness strategies to affect politeness perception have not previously been reported. Technology designers might find Tables 4 and 5 in this report especially useful for determining what politeness strategies are most appropriate for a given situation as well as what politeness strategies will evoke the desired feeling (autonomy or cooperation).
Fine-Tuned Large Language Model for Extracting Patients on Pretreatment for Lung Cancer from a Picture Archiving and Communication System Based on Radiological Reports
Authors: Yasaka Koichiro , Kanzawa Jun , Kanemaru Noriko , Koshino Saori , Abe Osamu 
Published: July 2024

Abstract
This study aimed to investigate the performance of a fine-tuned large language model (LLM) in extracting patients on pretreatment for lung cancer from picture archiving and communication systems (PACS) and comparing it with that of radiologists. Patients whose radiological reports contained the term lung cancer (3111 for training, 124 for validation, and 288 for test) were included in this retrospective study. Based on clinical indication and diagnosis sections of the radiological report (used as input data), they were classified into four groups (used as reference data): group 0 (no lung cancer), group 1 (pretreatment lung cancer present), group 2 (after treatment for lung cancer), and group 3 (planning radiation therapy). Using the training and validation datasets, fine-tuning of the pretrained LLM was conducted ten times. Due to group imbalance, group 2 data were undersampled in the training. The performance of the best-performing model in the validation dataset was assessed in the independent test dataset. For testing purposes, two other radiologists (readers 1 and 2) were also involved in classifying radiological reports. The overall accuracy of the fine-tuned LLM, reader 1, and reader 2 was 0.983, 0.969, and 0.969, respectively. The sensitivity for differentiating group 0/1/2/3 by LLM, reader 1, and reader 2 was 1.000/0.948/0.991/1.000, 0.750/0.879/0.996/1.000, and 1.000/0.931/0.978/1.000, respectively. The time required for classification by LLM, reader 1, and reader 2 was 46s/2539s/1538s, respectively. Fine-tuned LLM effectively extracted patients on pretreatment for lung cancer from PACS with comparable performance to radiologists in a shorter time.
Silicon-infused bacterial cellulose: in situ bioprocessing for tailored strength and surface characteristics
Authors: Greenhope Peregrine C. G. , Loh Joshua , Gilmour Katie A. , Zhang Meng , Haworth Luke , Xie Ming , Dade-Robertson Martyn , Jiang Yunhong 
Published: July 2024

AbstractIn this study we investigate the use of in situ bioprocessing for the production and surface modification of bacterial cellulose (BC) with silicon additives. The surface properties and tensile strength of the BC were studied and compared with plain BC. The effect the modification exhibited on the survivability of the bacteria was assessed by optical density measurements and found that the addition of the modification marginally slowed growth in the case of Tetramethyl orthosilicate (TMOS) and did not affect the growth in the case of Tetraethyl orthosilicate (TEOS). Characterisation of the modified BC was carried out using FTIR, EDX and confirmed the presence of silicon in the material. The width of fibres in the microstructure of BC was measured using SEM. Two different silicon modifications were used to modify the BC, it was shown that the TMOS modification decreased the tensile strength but that the TEOS increased the tensile strength of the BC fibres compared to plain BC. In addition, we found that the washing conditions of 1% NaOH (w/v), industrial methylated spirit (IMS), and deionised water (DI) showed some impact on the properties of the samples, particularly the IMS produced a reduced contact angle in the modified samples. However, the contact angle increased in the case of TEOS modification with the NaOH wash. In conclusion this study shows a novel method of modifying BC materials in-situ using silicon additives for increased tensile strength and the potential for tuneable hydro interactions.
Clinical features of retinal detachment treated with segmental scleral buckling
Authors: Wu Meng-Chiao , Lee Yi-Yang , Kuo Hsi-Kung 
Published: July 2024

Abstract
Purpose
Our study aims to evaluate the surgical outcomes and clinical features of retinal detachment (RD) cases treated with segmental scleral buckling (SB), elucidating the role of segmental SB as a vital option in specific situations during the current era.

Methods
We retrospectively reviewed 128 eyes with primary rhegmatogenous RD that underwent segmental scleral buckling between November 2008 and December 2020. Clinical features and success rates were recorded and analyzed.

Results
A total of 128 eyes were included. The patient’s ages ranged from 12 to 72 years, with a median age of 45. Most of the eyes were phakic (97%). Regarding the type of break, 47% were holes, and flap tears were found in 68 cases (53%). The break locations were superior-temporal (54%), inferior-temporal (31%), superior-nasal (9.5%), and inferior-nasal (5.5%). The length of the SB applied ranged from 3.5 to 8.0 clock hours, with a median of 6.0. Primary success was achieved in 121 eyes, and recurrence occurred in 7 eyes. All recurrent RD cases reattached after undergoing secondary VT. The causes of failure included 2 break reopens, 1 missed break, and 4 eyes with proliferative vitreoretinopathy. The single-surgery anatomic success (SSAS) rate for segmental SB was 94.5%. The final success rate was 100%.

Conclusions
For phakic, low complexity retinal detachment in our study, segmental scleral buckling emerges as a surgical option with a high primary success rate and a lower incidence of complications.
Heuristic approaches for non-exhaustive pattern-based change detection in dynamic networks
Authors: Loglisci Corrado , Impedovo Angelo , Calders Toon , Ceci Michelangelo 
Published: July 2024

AbstractDynamic networks are ubiquitous in many domains for modelling evolving graph-structured data and detecting changes allows us to understand the dynamic of the domain represented. A category of computational solutions is represented by the pattern-based change detectors (PBCDs), which are non-parametric unsupervised change detection methods based on observed changes in sets of frequent patterns over time. Patterns have the ability to depict the structural information of the sub-graphs, becoming a useful tool in the interpretation of the changes. Existing PBCDs often rely on exhaustive mining, which corresponds to the worst-case exponential time complexity, making this category of algorithms inefficient in practice. In fact, in such a case, the pattern mining process is even more time-consuming and inefficient due to the combinatorial explosion of the sub-graph pattern space caused by the inherent complexity of the graph structure. Non-exhaustive search strategies can represent a possible approach to this problem, also because not all the possible frequent patterns contribute to changes in the time-evolving data. In this paper, we investigate the viability of different heuristic approaches which prevent the complete exploration of the search space, by returning a concise set of sub-graph patterns (compared to the exhaustive case). The heuristics differ on the criterion used to select representative patterns. The results obtained on real-world and synthetic dynamic networks show that these solutions are effective, when mining patterns, and even more accurate when detecting changes.
Voices from within: Tracing Chinese Public Perceptions of Democracy in the New Era
Authors: Yang Duancheng , Zhou Yuan , Chu Yun-han 
Published: July 2024

AbstractDespite the Chinese Communist Party’s (CCP) emphasis on whole process people’s democracy and democracy with Chinese characteristics, the type of democracy desired by the Chinese people remains debatable. Using two nationwide surveys conducted in 2015 and 2019, this study examines how ordinary Chinese people view democracy during Xi Jinping’s new era. Acknowledging the constraints of conventional methodologies, this study adopts a novel approach that uses automated text analysis to dissect open-ended survey responses about democracy. The results reveal that Chinese citizens primarily associate democracy with freedom of speech, consultation, and minben rather than competitive elections. Furthermore, this study reveals that a higher level of education, more frequent internet usage, and higher household income are positively correlated with a liberal perspective on democracy. This suggests that although the Chinese communist regime may not face immediate pressure to implement open elections, growing support for consultation and freedom may necessitate a greater focus on procedural democracy building by the CCP. By supplementing traditional research methods, this study contributes to a better understanding of China’s regime resilience and its potential future trajectories.
Fear of cancer recurrence and change in hair cortisol concentrations in partners of breast cancer survivors
Authors: Fenech Alyssa L. , Soriano Emily C. , Asok Arun , Siegel Scott D. , Morreale Michael , Brownlee Hannah A. , Laurenceau Jean-Philippe 
Published: July 2024

Abstract
Purpose
Partners of breast cancer (BC) survivors report high rates of psychological distress including fear of cancer recurrence (FCR). Research suggests that partners may have poorer physical health outcomes than the general population, but little research has examined the physiological biomarkers by which distress may impact partner health outcomes. The current study examined the associations between FCR and changes in hair cortisol among BC partners.

Methods
Male partners (N = 73) of early-stage BC survivors provided hair samples during two visits, one after completion of survivors’ adjuvant treatment (T1) and again 6 months later (T2). Two subscales from the Fear of Cancer Recurrence Inventory and one subscale from the Concerns about Recurrence Scale comprised a latent FCR factor at T1. A latent change score model was used to examine change in cortisol as a function of FCR.

Results
Partners were on average 59.65 years of age (SD = 10.53) and non-Hispanic White (83%). Latent FCR at T1 was positively associated (b = 0.08, SE = 0.03, p = .004, standardized β = .45) with change in latent hair cortisol from T1 to T2.

Conclusions
Results indicated that greater FCR was associated with increases in hair cortisol in the months following adjuvant treatment. This is one of the first studies to examine the physiological correlates of FCR that may impact health outcomes in BC partners.

Implications for Cancer Survivors.
Findings highlight the need for further research into the relationship between FCR and its physiological consequences. Interventions to address partner FCR are needed and may aid in improving downstream physical health outcomes.
Just Around the Corner: How Cross-ethnic Solidarity Is Established Between Locals and the Asylum-Seekers and Refugees in a Local NGO
Authors: Ng Isabella 
Published: July 2024

AbstractThe paper examines how an NGO, established by a group of Hong Kong students, an academic (myself) and a group of asylum-seekers and refugees from South Asia, Southeast Asia and Africa, have worked through different barriers to create a network which builds on diversity to achieve cross-ethnic solidarity within the local community. It seeks to understand what makes a cross-ethnic solidarity network work in a local community. Using the concept of solidarity, the study looks into how the local NGO builds on a mix of diversity of race and gender and (non-)citizenship status to establish solidarity through community activities, advocacy and friendship. Based on ongoing ethnography between 2014 and 2020 and continuous activism work through 2014 until current, and interviews with 50 members of a local NGO, I argue that empowerment through solidarity needs to adopt a ‘neighbourhood’ approach based on mutual support, respect for diversity and empathy when building a solidarity network. An organization which takes on a ‘community’ and ‘neighbourhood’ approach by establishing friendship, focusing on relational aspect amongst local people and the asylum-seekers, is very effective in creating a lasting solidarity with trust. The study shows how solidarity built on differences could work effectively when migrants are in the host countries. More importantly, the results suggest that local population will need to take a more proactive approach to connect to the migrant community so as to build solidarity and achieve recognition and support from a wider community.
Implementing a new Research Data Alliance recommendation, the I-ADOPT framework, for the naming of environmental variables of continental surfaces
Authors: Charly Coussot , Isabelle Braud , Véronique Chaffard , Brice Boudevillain , Galle Sylvie 
Published: July 2024

AbstractTo improve data usage in an interdisciplinary context, a clear understanding of the variables being measured is required for both humans and machines. In this paper, the I-ADOPT framework, which decomposes variable names into atomic elements, was tested within the context of continental surfaces and critical zone science, characterized by a large number and variety of observed environmental variables. We showed that the I-ADOPT framework can be used effectively to describe environmental variables with precision and that it was flexible enough to be used in the critical zone science context. Variable names can be documented in detail while allowing alignment with other ontologies or thesauri. We have identified difficulties in modeling complex variables, such as those monitoring fluxes between different environmental compartments and for variables monitoring ratios of physical quantities. We also showed that, for some variables, different decompositions were possible, which could make alignments with other ontologies and thesauri more difficult. The precision of variable names proved inadequate for data discovery services and a non-standard label (SimplifiedLabel) had to be defined for this purpose. In the context of open science and interdisciplinary research, the I-ADOPT framework has the potential to improve the interoperability of information systems and the use of data from various sources and disciplines.
Analyzing Left-Truncated Samples with the Cox Model in the Presence of Missing Covariates
Authors: Vazquez Omar , Locke Hayley M. , Xie Sharon X. 
Published: July 2024

AbstractDelayed enrollment of subjects into a time-to-event study may result in a sample with biased outcome and covariate distributions. Additionally, missing covariate data may arise in these studies when information is difficult to collect due to patient burden or high testing costs. Some common missing data strategies, such as multiple imputation (MI) and augmented inverse probability weighting (AIPW), involve modeling the distribution of the missing covariate, which may be inaccurate with a left-truncated sample. Through simulation studies, we explore the performance of these methods in estimating Cox regression parameters under a variety of truncation and missing data scenarios. We find that MI and AIPW may be approximately unbiased when truncation is very low. Otherwise, biased estimation of the covariate distribution can cause MI to perform poorly. Similarly, AIPW may rely more heavily on correct estimation of the probability of having non-missing covariates in some settings. We apply these approaches to a Parkinson’s disease dementia biomarker study. In analyzing left-truncated data with missing covariates, careful consideration of the data characteristics and method assumptions is needed to obtain valid results.