bluuebunny/crossref_metadata_2025_split huggingface dataset:
Horseshoe Crab (Limulus polyphemus) Eggs Laid in Conditions Traditionally Considered Suboptimal Develop Differently than Those in Conditions Considered More Optimal
Authors: Quigley Thomas D. , Santangelo Nicholas 
Published: January 2025

Abstract
The American horseshoe crab, Limulus polyphemus, spawns in the intertidal zone and is thought to prefer conditions associated with the oxygen-rich sediments of the upper intertidal zone. However, they also spawn in the oxygen-poor sediments of the mid-intertidal zone. Such alternative habitats have traditionally been considered suboptimal, yet the consequences of spawning there are unknown. Here, eggs originating from upper- and mid-intertidal habitats were incubated under different abiotic conditions, and development rate and hatching success were measured. Eggs were collected in May 2021 from Long Island (Zach’s Bay), New York and incubated at two temperatures (26 °C and 20 °C), each coinciding with two O2 conditions (21% and 9.5% ambient). All eggs developed more slowly at the lower temperature and O2 levels, with eggs from the upper intertidal showing a greater decrease in rate than those from the mid-intertidal. Eggs from the upper intertidal had a higher hatching success overall which decreased with a lowering of either temperature or O2. The hatching success of eggs from the mid-intertidal only decreased when both variables were lower. Thus, eggs from upper intertidal habitats better use conditions traditionally considered optimal, while eggs from mid-intertidal habitats are hardier in conditions usually considered suboptimal. This could be due to differences in the adults that use each habitat or the developmental trajectory embryos take when exposed to particular habitat conditions. Either way, this novel finding suggests a strategy horseshoe crabs use to take advantage of environmental variation and underlies an adaptive response to alternative habitats once considered suboptimal.
A Low Complexity Efficient Deep Learning Model for Automated Retinal Disease Diagnosis
Authors: Chowa Sadia Sultana , Bhuiyan Md. Rahad Islam , Payel Israt Jahan , Karim Asif , Khan Inam Ullah , Montaha Sidratul , Hasan Md. Zahid , Jonkman Mirjam , Azam Sami 
Published: January 2025

Abstract
The identification and early treatment of retinal disease can help to prevent loss of vision. Early diagnosis allows a greater range of treatment options and results in better outcomes. Optical coherence tomography (OCT) is a technology used by ophthalmologists to detect and diagnose certain eye conditions. In this paper, human retinal OCT images are classified into four classes using deep learning. Several image preprocessing techniques are employed to enhance the image quality. An augmentation technique, called generative adversarial network (GAN), is utilized in the Drusen and DME classes to address data imbalance issues, resulting in a total of 130,649 images. A lightweight optimized compact convolutional transformers (OCCT) model is developed by conducting an ablation study on the initial CCT model for categorizing retinal conditions. The proposed OCCT model is compared with two transformer-based models: vision Transformer (ViT) and Swin Transformer. The models are trained and evaluated with 32 × 32 sized images of the GAN-generated enhanced dataset. Additionally, eight transfer learning models are presented with the same input images to compare their performance with the OCCT model. The proposed model’s stability is assessed by decreasing the number of training images and evaluating the performance. The OCCT model’s accuracy is 97.09%, and it outperforms the two transformer models. The result further indicates that the OCCT model sustains its performance, even if the number of images is reduced.
MCAD-MM: a benchmark dataset and method for multi-channel acoustic detection of marine mammals
Authors: Li Nan , Shi Zhensheng , Shao Yuqi , Sun Naizhe , Zheng Bing , Zheng Haiyong 
Published: January 2025

AbstractMarine mammals play an important role in maintaining the balance of marine ecosystems. A thorough understanding of their living habits, distribution, and environmental interaction is critical to formulating effective protection strategies. Traditional direct observation and sampling methods are limited in range, and it is challenging to cover the vast sea area because of the difficulties in operation and high cost. Passive acoustic monitoring (PAM) is an efficient and noninvasive technology to collect and analyze the sound signals of marine mammals via underwater deployment of hydrophones. However, the existing PAM studies are often limited by their focus on specific species or regions and need more universality. Moreover, the datasets and methods used need a unified benchmark, and the research results are difficult to integrate and share. This study proposes a novel approach based on deep learning (DL) to construct a common and systematic benchmark dataset and method for acoustic detection of marine mammals. The dataset is processed and analyzed via the combined use of the research data provided by the DCLDE Oahu dataset and the DL technology, covering acoustic signals and seawater ambient noises of various marine mammals, providing new data support and methodology guidance for research in related fields. The overall study involves a series of tasks: data acquisition, processing, labeling, methodology, experimental verification, and result analysis. We also use a Mamba-based state-space model for the detection of marine mammal vocalizations from the Mel-spectrogram representation of their acoustic signals. To the best of our knowledge, this is the first DL-based benchmark study on acoustic detection of marine mammals. This study not only provides a framework for the construction of a benchmark dataset and method based on a data-driven DL approach but also implements a unified process for data processing and quality assurance, laying a solid foundation for subsequent work and notably contributing to a broader understanding of marine biodiversity and conservation thereof.
Introducing youth to clinical research: Development and pilot implementation of a children’s activity book
Authors: Cadzow Renee , Strohmeier Andy , Carnes Erin , Quattrin Teresa 
Published: January 2025

Abstract

Introduction:
Children continue to be an underrepresented population in research and clinical trials due to difficulties encountered in recruitment, assenting, and retention processes. “Sofia Learns About Research” is a children’s activity book that introduces youth to clinical research and basic elements of clinical trials.

Methods:
Development of the activity book began in 2016, with publication of the first paper version in 2017 and an online version adapted for computer and tablet users in 2019. In 2019, we developed internal review board-approved pre/post surveys with five statements (written at ≤ 3rd-grade level) reflecting key concepts covered in the book. Participants were asked to indicate whether they agreed, disagreed, or were not sure about each of the statements and if they would ever want to be part of a research study. Preliminary analyses included descriptive statistics and cross-tabulations with chi squares.

Results:
Despite delays in dissemination and outreach due to the COVID-19 pandemic, we obtained feedback from over 170 diverse persons across a spectrum of communities and community partners. After book exposure, more participants knew that both children and parents have to assent/consent and that participants can withdraw from a study at any time.

Conclusions:
The book is an important advocacy tool with a long-term aim of increasing children’s knowledge and awareness about clinical research, ultimately leading to enhanced participation in clinical research and trials.
Assessing Mental Health Effects of Eastern Kentucky Households After the State’s Deadliest Flood: Using a Community Assessment for Public Health Emergency Response (CASPER)
Authors: Johnson Oshea , Balasuriya Lilanthi , Riley Tammy , Lockard Anthony Scott , Raleigh Angela , Ellis Martha , Schnall Amy Helene , Hanchey Arianna , Thoroughman Doug 
Published: January 2025

Abstract

Objectives
On July 28, 2022, eastern Kentucky experienced the state’s deadliest flood in recorded history. In response to ongoing mental health concerns from community members who survived the flood, local health department directors in affected communities requested technical assistance from the Kentucky Department for Public Health and the Centers for Disease Control and Prevention.

Methods
Two simultaneous Community Assessments for Public Health Emergency Response (CASPERs) were conducted 6 weeks after the flood. Four counties were assessed in each CASPER. EpiInfo7 was used to calculate the unweighted and weighted frequencies and percentages to estimate the number and percentage of households with a particular response in each CASPER.

Results
Approximately a third (30.5%) of households in CASPER 1 and approximately 40% of households in CASPER 2 reported experiencing ≥1 mental health problems. Individual-level mental health questions from a modified 3-stage CASPER found approximately 15% of persons in both CASPERs reported a Patient Health Questionnaire-2 (PHQ-2) score ≥3 and approximately 20% of persons in both CASPERs reported Generalized Anxiety Disorder-2 (GAD-2) score ≥3.

Conclusions
These findings indicated households experienced mental health problems after the flood. Depression and anxiety were prevalent among persons living in flood-affected areas. If ever needed, households preferred to receive mental health services in-person and locally.
Mediation Analysis in Bayesian Extended Redundancy Analysis with Mixed Outcome Variables
Authors: Choi Ji Yeh , Kyung Minjung , Park Ju-Hyun 
Published: January 2025

Abstract
Extended redundancy analysis (ERA) is a statistical approach to component-based multivariate regression modeling that explores interrelationships among multiple sets of while incorporating regression with a data-reduction technique. The extant models that utilize ERA have assumed the outcome variables with the same data type. Also, ERA models focused on estimating direct pathways only without explicitly addressing mediation effects. In this paper, ERA is extended to handle multiple mediators and mixed types of outcome variables by adopting a Bayesian framework, taking into account correlation structure among all of the outcome variables. The proposed method develops an algorithm that derives the joint posterior distribution of parameters using a Markov chain Monte Carlo algorithm. Simulations and an empirical dataset are provided to illustrate the usefulness of the proposed method.
Every Trait Counts: Marginal Maximum Likelihood Estimation for Novel Multidimensional Count Data Item Response Models with Rotation or
$\boldsymbol{\ell}_{\mathbf{1}}$
–Regularization for Simple Structure
Authors: Beisemann Marie , Holling Heinz , Doebler Philipp 
Published: January 2025

Abstract
Multidimensional item response theory (MIRT) offers psychometric models for various data settings, most popularly for dichotomous and polytomous data. Less attention has been devoted to count responses. A recent growth in interest in count item response models (CIRM)—perhaps sparked by increased occurrence of psychometric count data, e.g., in the form of process data, clinical symptom frequency, number of ideas or errors in cognitive ability assessment—has focused on unidimensional models. Some recent unidimensional CIRMs rely on the Conway–Maxwell–Poisson distribution as the conditional response distribution which allows conditionally over-, under-, and equidispersed responses. In this article, we generalize to the multidimensional case, introducing the Multidimensional Two-Parameter Conway–Maxwell–Poisson Model (M2PCMPM). Using the expectation-maximization (EM) algorithm, we develop marginal maximum likelihood estimation methods, primarily for exploratory M2PCMPMs. The resulting discrimination matrices are rotationally indeterminate. Recently, regularization of the discrimination matrix has been used to obtain a simple structure (i.e., a sparse solution) for dichotomous and polytomous data. For count data, we also (1) rotate or (2) regularize the discrimination matrix. We develop an EM algorithm with lasso (

$\ell _1$

) regularization for the M2PCMPM and compare (1) and (2) in a simulation study. We illustrate the proposed model with an empirical example using intelligence test data.
Identifiability of Polychoric Models with Latent Elliptical Distributions
Authors: Cheng Che , Yang Hau-Hung , Hsu Yung-Fong 
Published: January 2025

Abstract
The family of polychoric models (PM) categories ordinal data with latent multivariate normal variables. This modeling framework is commonly used to study the association between ordinal variables, often leading to a polychoric correlation model (PCM). Moreover, PM subsumes several well-known psychometric models, such as the structural equation modeling (SEM) with ordinal data. That said, the identifiability of PM has not been addressed in the literature. Meanwhile, in recent years researchers have suggested that the latent variables underlying PM could be generalized to the family of elliptical distributions, such as the multivariate logistic and t distributions. This article concerns the identifiability of PM and PCM with latent elliptical distributions, for which we show that PM is not identifiable and PCM is identifiable. In particular, we prove the identifiability of the polychoric t correlation model based on the copula representation. We then move on to find the set of identifiability constraints of PM through an “equivalence-classes approach of identifiability,” and demonstrate its use in two applications: one concerns the identifiability of PM on Likert scales and on comparative judgment, and the other concerns the identifiability of ordinal SEM and item factor analysis. Possible implications induced by these identifiability constraints are discussed.
The Sum Score Model: Specifying and Testing Equally Weighted Composites Using Structural Equation Modeling
Authors: Schuberth Florian , Schamberger Tamara , Kemény Ildikó , Henseler Jörg 
Published: January 2025

Abstract
In principle, structural equation modeling (SEM) is capable of emulating all approaches based on the general linear model. Yet, modeling sum scores in a structural equation model is not straightforward. Existing approaches to studying sum scores in a structural equation model are limited in terms either of model specification or of model assessment. This paper introduces a specification to SEM that allows for directly modeling sum scores and that overcomes existing approaches’ limitations in dealing with sum scores in the SEM context. The sum score model we present builds on the recently proposed refined Henseler–Ogasawara (H–O) specification of composites. It allows us to estimate models with sum scores in an integrative way. It can mimic the results of existing approaches and provides a means of assessing whether a sum score fully transmits the effects of or on the variables that make up the sum score. In addition, it allows for taking into account random measurement error in the variables that form the sum score. Consequently, this model specification offers researchers an improved way of judging and defending the use of sum scores empirically and conceptually.
Assumptions and Properties of Two-Level Nonparametric Item Response Theory Models
Authors: Koopman Letty , Zijlstra Bonne J. H. , van der Ark L. Andries 
Published: January 2025

Abstract
Nonparametric item response theory (IRT) models consist of assumptions that restrict the joint item-score distribution. These assumptions imply stochastic ordering properties that allow ordering of respondents and items using the simple sum score and item mean score, respectively, and imply observable data properties that are useful for investigating model fit. In this paper, we investigate these properties for two-level nonparametric IRT. We introduce four two-level nonparametric IRT models. Two models pertain to respondents nested in groups: The MHM-1, useful for ordering respondents and groups, and the DMM-1, useful for ordering respondents, groups, and items. Two models pertain to groups rated by multiple respondents: The MHM-2, useful for ordering groups, and the DMM-2, useful for ordering groups and items. We define the model assumptions, derive implied stochastic ordering properties, and derive observable data properties that are useful for model fit investigation. Relations between models and properties are also presented.
Robust approximate constraint following control design for collaborative robots system and experimental validation
Authors: Liu Haohua , Zhen Shengchao , Liu Xiaoli , Zheng Hongmei , Gao Liansheng , Chen Ye-Hwa 
Published: January 2025

AbstractThe paper presents a novel control method aimed at enhancing the trajectory tracking accuracy of two-link mechanical systems, particularly nonlinear systems that incorporate uncertainties such as time-varying parameters and external disturbances. Leveraging the Udwadia–Kalaba equation, the algorithm employs the desired system trajectory as a servo constraint. First, the system’s constraints to construct its dynamic equation and apply generalized constraints from the constraint equation to an unconstrained system. Second, we design a robust approximate constraint tracking controller for manipulator control and establish its stability using Lyapunov’s law. Finally, we numerically simulate and experimentally validate the controller on a collaborative platform using model-based design methods.
Gender representation in undergraduate music technology education: case studies from Aotearoa/New Zealand
Authors: Hoad Catherine , Johnson Henry , Rogerson-Berry Megan , Wilson Oli , Ellery Josh 
Published: January 2025

Abstract
This article explores the relationship between gender inequities in undergraduate music technology education and the widespread imbalances that permeate the professional music technology workforce. We present evidence concerning the relationship between tertiary training and industry outcomes by focusing on three music technology degree-level offerings in Aotearoa/New Zealand. In doing so, we critically examine the ways in which higher education in Aotearoa/New Zealand may be seen to perpetuate international trends concerning the underrepresentation of women in music technology fields. Firstly, the article offers an overview of extant scholarship on gender and music technology training. From here, it examines national data on music enrolments that show gender imbalances across music degrees. It then analyses three music technology degree-level offerings in Aotearoa as case studies. These reveal how gender inequities are amplified in areas relating to music technology. Datasets are then considered in relation to gender representation within the music industry in Aotearoa. The article concludes by offering reflections on key areas for interventions and avenues for further research.
Neural Voices of Patients with Severe Brain Injury?
Authors: Owen Matthew , Hight Darren , Hudetz Anthony G. 
Published: January 2025

Abstract
Studies have shown that some covertly conscious brain-injured patients, who are behaviorally unresponsive, can reply to simple questions via neuronal responses. Given the possibility of such neuronal responses, Andrew Peterson et al. have argued that there is warrant for some covertly conscious patients being included in low-stakes medical decisions using neuronal responses, which could protect and enhance their autonomy. The justification for giving credence to alleged neuronal responses must be analyzed from various perspectives, including neurology, bioethics, law, and as we suggest, philosophy of mind. In this article, we analyze the warrant for giving credence to neuronal responses from two different views in philosophy of mind. We consider how nonreductive physicalism’s causal exclusion problem elicits doubt about interpreting neural activity as indicating a conscious response. By contrast, such an interpretation is supported by the mind-body powers model of neural correlates of consciousness inspired by hylomorphism.
Ethical Considerations and Implications of Multi-Cancer Early Detection Screening: Reliability, Access and Cost to Test and Treat
Authors: Sempere Lorenzo F. 
Published: January 2025

Abstract
This essay focuses on the ethical considerations and implications of providing a universal multi-cancer screening test as the best approach to reduce societal cancer burden in a society with limited funds, resources, and infrastructure. With 1.9 million cancer diagnoses each year in the United States, with 86% of all cancers diagnosed in individuals over the age of 50, and with screening tools approved for only four cancer types (breast, cervical, colorectal, and lung cancer), it seems that a multi-cancer screening test to detect most cancer early that is easy to administer, and is accurate and cost-effective, would be worth considering. Whole-body magnetic resonance imaging and a multi-marker blood test are the two main technologies that we will discuss as a universal screening test. However, to understand and appreciate the societal and clinical breakthrough of such a screening test, we must first consider the accessibility and efficacy of current screening methods. We conclude with a closer examination of the ethical implications of implementing the Galleri test as a multi-cancer detection screening tool as adamantly advocated by the company that developed this blood-based test.
Contiguous Xp21 deletion involving Duchenne muscular dystrophy and McLeod neuroacanthocytosis syndrome results in rapidly progressive and fatal cardiomyopathy
Authors: Blackstone William Z. , Malaguit Seth E. , Shwaish Natalie S. , Frandsen Erik L. 
Published: January 2025

AbstractDilated cardiomyopathy is an expected manifestation and common cause of death in patients with Duchenne muscular dystrophy. We present an unusually rapid progression of cardiomyopathy in a boy with Duchenne muscular dystrophy. Expanded genetic testing revealed a contiguous Xp21 deletion involving dystrophin and XK genes, responsible for Duchenne muscular dystrophy and McLeod neuroacanthocytosis syndrome, respectively, resulting in a more severe cardiac phenotype.
The Interpretation of Karakoram Anomaly by High Karakoram Ice Core Record
Authors: Wang Jiajia , Xu Baiqing , Li Zhen , Nasir Jawad , Farhan Suhaib Bin , Wang Mo , Xie Ying , Yang Song , Cauquoin Alexandre , Hussain Azfar 
Published: January 2025

AbstractGlacier mass in the Karakoram region has increased since the mid‐1990s in contrast to Himalayan glaciers. The scarcity and brevity of observations, including in situ observations and weather station data, limit our understanding of the Karakoram anomaly. Here, we present the stable water isotopes and snow accumulation records of Karakoram's first shallow ice core, covering 1998 to 2018. The decreased δ18O suggests an overall cooling trend in the Karakoram region for the early 21st century. In contrast, snow accumulation shows a significant increasing trend, which reveals that the Karakoram glacier has been getting more and more precipitation supplies recently. Using correlation field analysis and a backward trajectory model, we find that the decreased temperature and increased precipitation in the Karakoram are related to weakening westerlies. The northward shift of westerlies will transport colder water vapor from the North Atlantic to Karakoram, decreasing temperature and increasing precipitation in Karakoram. Our results indicated a trend of temperature cooling and precipitation increase trend in the Karakoram related to the weakened westerlies and the North Atlantic, which might explain the Karakoram anomaly that occurred in the Pamir‐Karakoram‐Kunlun region under the present climate change background. These results can provide new insights for predicting the changes in glacier mass in Karakoram and evaluating the variations in Asian water towers.