bluuebunny/crossref_metadata_2025_split huggingface dataset:
Machine Learning for Prediction of Childhood Mental Health Problems in Social Care
Authors: Crowley Ryan , Parkin Katherine , Rocheteau Emma , Massou Efthalia , Friedmann Yasmin , John Ann , Sippy Rachel , Liò Pietro , Moore Anna 
Published: May 2024

BackgroundRates of childhood mental health problems are increasing in the United Kingdom. Early identification of childhood mental health problems is challenging but critical to future psycho-social development of children, particularly those with social care contact. Clinical prediction tools could improve these early identification efforts.AimsCharacterise a novel cohort of children in social care and develop and validate effective Machine Learning (ML) models for prediction of childhood mental health problems.MethodWe used linked, de-identified data from the Secure Anonymised Information Linkage (SAIL) Databank to create a cohort of 26,820 children in Wales, UK, receiving social care services. Integrating health, social care, and education data, we developed several ML models. We assessed the performance, interpretability, and fairness of these models.ResultsRisk factors strongly associated with childhood mental health problems included substance misuse, adoption disruption, and autism. The best-performing model, a Support Vector Machine (SVM) model, achieved an area under the receiver operating characteristic curve (AUROC) of 0.743, with 95% confidence intervals (CI) of 0.724-0.762. Assessments of algorithmic fairness showed potential biases within these models.ConclusionML performance on this prediction task was promising but requires refinement before clinical implementation. Given its size and diverse data, the SAIL Databank is an important childhood mental health database for future work.
Preparing for the Postpartum Medicaid Extension Evaluation in Illinois
Authors: Handler Arden , Krause Caitlin Meyer , Rankin Kristin 
Published: May 2024

AbstractBackgroundGiven increased attention to the maternal health crisis, its heavy toll on Black birthing persons, and recognition that most pregnancy-related deaths occur in the extended postpartum period, Illinois implemented a Postpartum Medicaid Extension (PME).ObjectivesThe purpose of this study was to use baseline data from the pre-PME period to ascertain which groups of eligible persons can be expected to experience improved outcomes as a result of PME implementation in Illinois.MethodsWe focused on the Well-Woman Visit (WWV) as it can be measured in the later postpartum period and is relevant for non-pregnant women. We provide baseline prevalence estimates for WWV receipt using PRAMS and BRFSS data within income strata and within combined income-insurance strata with particular attention to the income group most likely to be affected by the PME. Using multivariable binomial regression, we generate adjusted prevalence differences across income and insurance strata overall and by race/ethnicity.ResultsThe Illinois PME has the potential to improve the receipt of well-woman care in the 138-213% FPL income group, the group most likely to be affected by the PME. The analysis also suggests that Black women in Illinois may not be the group most likely to benefit from the enhanced access to care made available by the PME without additional focused attention to their particular needs and experiences. Conclusions and Implications for Practice and Policy:The PME is necessary but not sufficient for addressing racial/ethnic inequities in maternal health. Leveraging the opportunity that the PME provides to design and support delivery models that maximize the effects of such coverage will be essential to address the maternal health crisis in Illinois. Without extra attention to the needs and experiences of Black women, focused on the delivery of care as well as the structural determinants of health including institutional and interpersonal racism, the benefits of the PME for Black women may not be fully realized.Recommendations for PracticeAn extensive outreach and communications campaign focused on the Postpartum Medicaid Extension in Illinois is necessary so that all birthing persons covered by Medicaid and their providers know that postpartum coverage now continues through 12 months.New approaches to care are needed to maximize the benefits of the PME, including elevation of the medical care home for women’s primary/interconception care, efforts to reduce provider bias in the delivery of care, and implementation of postpartum care models such as the Two-Generation approach.Development of a Performance Measurement System within Medicaid focused on the extended postpartum period is essential to ensure that Medicaid providers are aware of the PME and pay particular attention to the extended postpartum period.Promotion of enhanced postpartum care packages that include reimbursement for services to address the structural/social determinants of health is essential.
Empirical Sample Size Determination for Popular Classification Algorithms in Clinical Research
Authors: Silvey Scott , Liu Jinze 
Published: May 2024

AbstractMotivationThe performance of a classification algorithm eventually reaches a point of diminishing returns, where additional sample added does not improve results. Thus, there is a need for determining an optimal sample size that both maximizes performance, while accounting for computational burden or budgetary concerns.MethodsSixteen large open-source datasets were collected, each containing a binary clinical outcome. Four machine learning algorithms were assessed: XGBoost (XGB), Random Forest (RF), Logistic Regression (LR), and Neural Networks (NN). For each dataset, the cross-validated AUC was calculated at increasing sample sizes, and learning curves were fit. Sample sizes needed to reach the full-dataset AUC minus 2% (or, 0.02) were calculated from the fitted learning curves and compared across the datasets and algorithms. Dataset-level characteristics: minority class proportion, full-dataset AUC, strength/number/type of features, and degree of nonlinearity, were examined. Negative binomial regression models were used to quantify relationships between these characteristics and expected sample sizes within each algorithm. Four multivariable models were constructed which selected the best combination of dataset-specific characteristics that minimized out-of-sample prediction error. Additional models were fitted which allowed for prediction of the expected gap in performance at a given sample size using the same empirical learning curve data.ResultsAmong the sixteen datasets (full-dataset sample sizes ranging from 70,000-1,000,000), median sample sizes were 9,960 (XGB), 3,404 (RF), 696 (LR), and 12,298 (NN) to reach AUC convergence. For all four algorithms, more balanced classes (multiplier: 0.93-0.96 for 1% increase in minority class proportion) were associated with decreased sample size. Other characteristics varied in importance across algorithms - in general, more features, weaker features, and more complex relationships between the predictors and the response increased expected sample sizes. In multivariable analysis, top selected predictors were minority class proportion, full-dataset AUC, and dataset nonlinearity (XGB and RF). For LR, top predictors were minority class proportion, percentage of strong linear features, and number of features. For NN, top predictors were minority class proportion, percentage of numeric features, and dataset nonlinearity.ConclusionsThe sample sizes needed to reach convergence among four popular classification algorithms vary by dataset and method and are associated with dataset-specific characteristics that can be influenced or estimated prior to the start of a research study.
Locomotion Efficiency of Elephants: Mechanical work and energetics
Authors: Genin Joakim G. , Dewolf Arthur H. , Willems Patrick A. , Heglund Norman C 
Published: May 2024

AbstractThe size scaling of energy expenditure during locomotion has long puzzled researchers seeking biological invariants. Existing data, most of it from animals smaller than 200 kg, show that the mass specific mechanical work of locomotion is nearly independent of size, yet the metabolic cost of locomotion decreases with increasing size. Major questions remain concerning heavier animals: extrapolating mechanical work and metabolic cost to animals the size of an elephant comes to the uncomfortable conclusion that elephants would produce more energy than they would consume. Our study addresses this longstanding size-scaling conundrum by focusing on the locomotion mechanics and energetics of elephants, the largest extant land animals. In this study, the work required to move the limbs relative to the centre of mass of the whole body (COM) was measured in 27 Asian elephants (872-4000 kg). The total mechanical work was calculated by adding the external work required to maintain the movements theCOM. Our investigation challenges the belief that mass-specific mechanical work of locomotion is independent of size. Furthermore, our study unveils a surprising aspect of elephant locomotion—the substantial fraction of total mechanical work dedicated to limb swinging. At high speeds, elephants allocate ∼75% of their work to limb swinging. Also, by quantifying the metabolic cost and mechanical work, we provide the first estimation of the efficiency of converting metabolic energy into mechanical work in elephants. Elephants exhibit an efficiency of at best 45%, which is similar to that in other large mammals such as horses and humans. This study not only addresses a significant gap in our understanding of size scaling in locomotion but also opens new avenues for exploring the evolution of energy expenditure across diverse animal sizes.
MAIA: An open-source, modular, bioreactor for cities
Authors: Rico Andres , Kong David , Larson Kent 
Published: May 2024

AbstractThis work presents the design and fabrication of MAIA, an open-source, modular, low-cost, and portable bioreactor for democratizing the development of synthetic biology based projects for urban settings. The integration of open-source synthetic biology (synbio) tools in a city’s infrastructure planning and design is crucial for addressing the great challenges related to urbanization. Synbio tools have great potential to help us complement our current sensing and actuating urban infrastructure. The MAIA reactor controls bacterial growth variables, making it suitable for cell-based experiments while reducing the need for expensive laboratory equipment. Its low-cost and open-source design allow for easy replication and modification, making it accessible to a broader audience. Its portability makes it suitable for use outside of traditional laboratory settings. We qualitatively and quantitatively validated the reactor’s capability to support cell growth, stimulate gene expression, and act as a creative tool for students and users.
A Deep Learning-Based Segmentation of Cells and Analysis (DL-SCAN)
Authors: Bhattarai Alok , Meyer Jan , Petersilie Laura , Shah Syed I , Rose Christine R. , Ullah Ghanim 
Published: May 2024

AbstractWith the recent surge in the development of highly selective probes, fluorescence microscopy has become one of the most widely used approaches to study cellular properties and signaling in living cells and tissues. Traditionally, microscopy image analysis heavily relies on manufacturer-supplied software, which often demands extensive training and lacks automation capabilities for handling diverse datasets. A critical challenge arises, if fluorophores employed exhibit low brightness and low Signal-to-Noise ratio (SNR). As a consequence, manual intervention may become a necessity, introducing variability in the analysis outcomes even for identical samples when analyzed by different users. This leads to the incorporation of blinded analysis which ensures that the outcome is free from user bias to a certain extent but is extremely time-consuming. To overcome these issues, we have developed a tool called DL-SCAN that automatically segments and analyzes fluorophore-stained regions of interest such as cell bodies in fluorescence microscopy images using a Deep Learning algorithm called Stardist. We demonstrate the program’s ability to automate cell identification and study cellular ion dynamics using synthetic image stacks with varying SNR. This is followed by its application to experimental Na+and Ca2+imaging data from neurons and astrocytes in mouse brain tissue slices exposed to transient chemical ischemia. The results from DL-SCAN are consistent, reproducible, and free from user bias, allowing efficient and rapid analysis of experimental data in an objective manner. The open-source nature of the tool also provides room for modification and extension to analyze other forms of microscopy images specific to the dynamics of different ions in other cell types.Statement of SignificanceFluorescence microscopy is widely used to study the functional and morphological features of living cells. However, various factors, such as low SNR, background noise, drift in the signal, movement of the tissue, and the large size of the resulting imaging data, make the processing of fluorescence microscopy data prone to errors, user bias, and extremely time-consuming. These and other issues hinder the full utilization of these powerful experimental techniques. Our novel Deep Learning-based tool overcomes these issues by processing and analyzing fluorescence imaging data, e.g., enabling automated visualization of ion changes in living cells in brain slices. Yet the tool remains easy to use with a streamlined workflow.
Development of the overlapping network modules in the human brain
Authors: Lei Tianyuan , Liao Xuhong , Liang Xinyuan , Sun Lianglong , Xia Mingrui , Xia Yunman , Zhao Tengda , Chen Xiaodan , Men Weiwei , Wang Yanpei , Ma Leilei , Liu Ningyu , Lu Jing , Zhao Gai , Ding Yuyin , Deng Yao , Wang Jiali , Chen Rui , Zhang Haibo , Tan Shuping , Gao Jia-Hong , Qin Shaozheng , Tao Sha , Dong Qi , He Yong 
Published: May 2024

AbstractDevelopmental connectomic studies have shown that the modular organization of functional networks in the human brain undergoes substantial reorganization with age to support cognitive growth. However, these studies implicitly assume that each brain region belongs to one and only one specific network module, ignoring the potential spatial overlap between functional modules. How the overlapping functional modular architecture develops and whether this development is related to structural signatures remain unknown. Using longitudinal multimodal structural, functional, and diffusion MRI data from 305 children (aged 6–14 years), we investigated the development of the overlapping modular architecture of functional networks, and further explored their structural associations. Specifically, an edge-centric network model was used to identify the overlapping functional modules, and the nodal overlap in module affiliations was quantified using the entropy measure. We showed a remarkable regional inhomogeneity in module overlap in children, with higher entropy in the ventral attention, somatomotor, and subcortical networks and lower entropy in the visual and default-mode networks. Furthermore, the overlapping modules developed in a linear, spatially dissociable manner from childhood to adolescence, with significantly reduced entropy in the prefrontal cortex and putamen and increased entropy in the parietal lobules. Personalized overlapping modular patterns capture individual brain maturity as characterized by brain age. Finally, the overlapping functional modules can be significantly predicted by integrating gray matter morphology and white matter network properties. Our findings highlight the maturation of overlapping network modules and their structural substrates, thereby advancing our understanding of the principles of connectome development.
Tri-omic mapping revealed concerted dynamics of 3D epigenome and transcriptome in brain cells
Authors: Chai Haoxi , Huang Xingyu , Xiong Guangzhou , Huang Jiaxiang , Pels Katarzyna Karolina , Meng Lingyun , Han Jin , Tang Dongmei , Pan Guanjing , Deng Liang , Xiao Qin , Wang Xiaotao , Zhang Meng , Banecki Krzysztof , Plewczynski Dariusz , Wei Chia-Lin , Ruan Yijun 
Published: May 2024

AbstractExploring the genomic basis of transcriptional programs has been a longstanding research focus. Here, we report a high-throughput single-cell tri-omic method to capturechromatinaccessibility, interaction, andRNA simultaneously (ChAIR). After validating in cultured cells, we applied ChAIR to brain cells across mouse lifespan and delineated the concerted dynamics of 3D-epigenomic architecture and transcription during maturation and aging. Particularly, ultra-long chromatin megacontacts and promoter-associated 3D-epigenomic states are effective in defining cell identity and revealing spatially-resolved anatomic specificity. Importantly, we found that neurons in different brain regions and non-neuronal cells may undergo divergent genomic mechanisms during differentiation and aging. Our results demonstrated ChAIR’s robustness of connecting chromatin folding architecture with cellular property and its potential applications to address complex questions in single-cell resolution and spatial specificity.
Genomes of two indigenous clamsAnomalocardia flexuosa(Linnaeus, 1767) andMeretrix petechialis(Lamarck, 1818)
Authors: Law Sean Tsz Sum , Nong Wenyan , Au Ming Fung Franco , Cheung Leni Hiu Tong , Shum Cheryl Wood Yee , Lee Shing Yip , Cheung Siu Gin , Hui Jerome Ho Lam 
Published: May 2024

AbstractClam digging has a long history in Hong Kong, but unregulated clam digging activities depletes clam populations and threatens the ecosystem. Population genomics is useful to unravel the connectivity of clams at different geographical locations and to provide necessary conservation measures; and yet, only limited number of clams in Hong Kong have genomic resources. Here, we present chromosomal-level genome assemblies for two clams commonly found in Hong Kong,Anomalocardia flexuosaandMeretrix petechialis, using a combination of PacBio HiFi and Omni-C reads. We assembled each genome (∼1.04-1.09 Gb) into 19 pseudochromosomes with high sequence continuity (scaffold N50 = 58.5 Mb and 53.5 Mb) and high completeness (BUSCO scores 94.4% and 95.7%). A total of 20,881 and 20,084 gene models were also predicted forA. flexuosaandM. petechialisrespectively using transcriptomes generated in this study. The two new genomic resources established in this study will be useful for further study of the biology, ecology, and evolution of clams, as well as setting up a foundation for evidence-informed decision making in conservation measures and implementation.
Effects of cognitive load and years of experience on phase-amplitude coupling in simultaneous interpretation
Authors: Yagura Haruko , Tanaka Hiroki , Nakamura Satoshi 
Published: May 2024

AbstractSimultaneous interpretation is a highly cognitively demanding task that requires constant attention switching between languages. Interest continues to grow in the contribution of phase-amplitude coupling (PAC), which involves the cooperative interaction of multiple oscillations and working memory. In this study, we established subjective definitions for cognitive load levels based on the subjective word familiarity of simultaneous interpretation, categorizing them as low, medium, or high. We then compared the changes in the PAC patterns between experienced interpreters and beginners. Experienced interpreters exhibited an increase in PAC, including theta-gamma PAC, which is linked to working memory, as well as theta-beta PAC, alpha-beta PAC and alpha-gamma PAC, with rising cognitive load levels in simultaneous interpreting. This suggests that experienced simultaneous interpreters choose a more adaptive neural processing strategy in response to the cognitive demands of interpretation language. In contrast, beginner interpreters do not show such changes in PACs, indicating either an underdeveloped or a different neurological approach to the cognitive load levels of interpretation language. The difference in PAC responses between the two groups reflects varying cognitive and interpretive strategies in the brain, where experienced interpreters might utilize more advanced neural mechanisms to manage higher levels of difficulty in simultaneous interpretation.
Flexible Cell Modeling Using Frequency Modulation
Authors: Jacob Jerry , Patel Nitish , Sehgal Sucheta 
Published: May 2024

AbstractComputational models of the cell can be used to study the impact of drugs and assess pathological risks. Typically, computational models are computationally demanding or difficult to implement in dedicated hardware for real-time emulation. A new Frequency Modulation (FM) model is proposed to address these limitations. This new model utilizes a single sine generator with constant amplitude, but phase/frequency is modulated to emulate an action potential (AP). The crucial element of this model is the identification of the modulating signal. Focusing on FPGA implementation, we have utilized a piecewise linear polynomial with a fixed number of breakpoints to serve as a modulating signal. The ability to adapt this modulating signal permits the emulation of dynamic properties and coupling of cells. We have introduced a state controller that handles both of these requirements. The building blocks of the FM model have direct integer equivalents and are amenable to implementation on digital platforms like Field Programming Gate Arrays (FPGA).We have demonstrated wavefront propagation of our model in 1-D and 2-D models of a tissue. Various parameters were used to quantify the wavefront propagation in 2-D tissues. We also emulate some cellular dysfunctions. The FM model can replicate any detailed cell model and emulate its corresponding tissue model. Overall, the results depict that the FM model has the potency for real-time cell and tissue emulation on an FPGA.
Fibre type differences in the organisation of mononuclear cells and myonuclei at the tips of human myofibres
Authors: Hoegsbjerg Christian , Møbjerg Ask , Yeung Ching-Yan Chloé , Schjerling Peter , Krogsgaard Michael R. , Koch Manuel , Kjaer Michael , von Keudell Arvind G. , Mackey Abigail L. 
Published: May 2024

AbstractThe myotendinous junction (MTJ) is a weak link in the musculoskeletal system. Here, we isolated the tips of single myofibres from healthy human hamstring muscles for confocal microscopy (n=6) and RNAscopein situhybridization (n=6) to gain insight into the profiles of cells and myonuclei in this region. A marked presence of mononuclear cells was observed coating the fibre tips, with a median of 29 (range 16-63) and 16 (9-23) cells per fibre for type I and II myofibres, respectively (p<0.05). The number and density of myonuclei gradually increased from the myofibre proper towards the tip (p<0.05), similarly for both fibre types, and a greater number ofCOL22A1-expressing nuclei was seen in type II vs type I myofibres (p<0.05). These divergent fibre type-specific characteristics of the MTJ reflect the respective demands for remodelling of the tendon and myofibre sides of the junction according to loading patterns. This insight refines our fundamental understanding of the human MTJ at the cell and structural levels.Summary statementAt the site of attachment to tendon, type I and II human myofibre tips display divergent numbers of mononuclear cells and COL22A1+ nuclei, changing our understanding of myotendinous junction biology.