bluuebunny/crossref_metadata_2025_split huggingface dataset:
Targeted learning with an undersmoothed LASSO propensity score model for large-scale covariate adjustment in health-care database studies
Authors: Wyss Richard , van der Laan Mark , Gruber Susan , Shi Xu , Lee Hana , Dutcher Sarah K , Nelson Jennifer C , Toh Sengwee , Russo Massimiliano , Wang Shirley V , Desai Rishi J , Lin Kueiyu Joshua 
Published: March 2024

Abstract
Least absolute shrinkage and selection operator (LASSO) regression is widely used for large-scale propensity score (PS) estimation in health-care database studies. In these settings, previous work has shown that undersmoothing (overfitting) LASSO PS models can improve confounding control, but it can also cause problems of nonoverlap in covariate distributions. It remains unclear how to select the degree of undersmoothing when fitting large-scale LASSO PS models to improve confounding control while avoiding issues that can result from reduced covariate overlap. Here, we used simulations to evaluate the performance of using collaborative-controlled targeted learning to data-adaptively select the degree of undersmoothing when fitting large-scale PS models within both singly and doubly robust frameworks to reduce bias in causal estimators. Simulations showed that collaborative learning can data-adaptively select the degree of undersmoothing to reduce bias in estimated treatment effects. Results further showed that when fitting undersmoothed LASSO PS models, the use of cross-fitting was important for avoiding nonoverlap in covariate distributions and reducing bias in causal estimates.
A model-based hierarchical Bayesian approach to Sholl analysis
Authors: VonKaenel Erik , Feidler Alexis , Lowery Rebecca , Andersh Katherine , Love Tanzy , Majewska Ania , McCall Matthew N 
Published: March 2024

Abstract

Motivation
Due to the link between microglial morphology and function, morphological changes in microglia are frequently used to identify pathological immune responses in the central nervous system. In the absence of pathology, microglia are responsible for maintaining homeostasis, and their morphology can be indicative of how the healthy brain behaves in the presence of external stimuli and genetic differences. Despite recent interest in high throughput methods for morphological analysis, Sholl analysis is still widely used for quantifying microglia morphology via imaging data. Often, the raw data are naturally hierarchical, minimally including many cells per image and many images per animal. However, existing methods for performing downstream inference on Sholl data rely on truncating this hierarchy so rudimentary statistical testing procedures can be used.

Results
To fill this longstanding gap, we introduce a parametric hierarchical Bayesian model-based approach for analyzing Sholl data, so that inference can be performed without aggressive reduction of otherwise very rich data. We apply our model to real data and perform simulation studies comparing the proposed method with a popular alternative.

Availability and implementation
Software to reproduce the results presented in this article is available at: https://github.com/vonkaenelerik/hierarchical_sholl. An R package implementing the proposed models is available at: https://github.com/vonkaenelerik/ShollBayes.
Is Work–Family Enrichment Important to Social Workers’ Life Meaning? A Serial Mediation Model of Self-Efficacy and Work Engagement
Authors: Jiang Chaoxin , Jiang Shan 
Published: March 2024

Abstract
Prior research has predominantly centred on the negative conflicts of the interplay between work and family obligations, with limited attention paid to the concept of work–family enrichment, particularly within the context of social work. Furthermore, little is known about the underlying mechanisms through which work–family enrichment influences social workers’ life meaning. To address these gaps, this study endeavours to scrutinise the serial mediation pathway involving self-efficacy and work engagement in elucidating the relationship between work–family enrichment and life meaning. A sample of 1,023 social workers (mean age = 36.24) is methodically selected through a multistage cluster random sampling approach in Hangzhou City, China. The empirical results provide evidence supporting a serial mediation path from self-efficacy with work engagement is posited to operate between work–family enrichment and life meaning. These findings hold both theoretical and practical significance, offering insights relevant to social workers, supervisors and their organisations.
Effects of accelerated intermittent theta-burst stimulation in modulating brain of Alzheimer’s disease
Authors: Lin Hua , Liang Junhua , Wang Qianqian , Shao Yuxuan , Song Penghui , Li Siran , Bai Yang 
Published: March 2024

AbstractIntermittent theta-burst stimulation (iTBS) is emerging as a noninvasive therapeutic strategy for Alzheimer's disease (AD). Recent advances highlighted a new accelerated iTBS (aiTBS) protocol, consisting of multiple sessions per day and higher overall pulse doses, in brain modulation. To examine the possibility of applying the aiTBS in treating AD patients, we enrolled 45 patients in AD at early clinical stages, and they were randomly assigned to either receive real or sham aiTBS. Neuropsychological scores were evaluated before and after treatment. Moreover, we detected cortical excitability and oscillatory activity changes in AD, by the single-pulse TMS in combination with EEG (TMS-EEG). Real stimulation showed markedly better performances in the group average of Auditory Verbal Learning Test scores compared to baseline. TMS-EEG revealed that aiTBS has reinforced this memory-related cortical mechanism by increasing cortical excitability and beta oscillatory activity underlying TMS target. We also found an enhancement of local natural frequency after aiTBS treatment. The novel findings implicated that high-dose aiTBS targeting left DLPFC is rapid-acting, safe, and tolerable in AD patients. Furthermore, TMS-related increase of specific neural oscillation elucidates the mechanisms of the AD cognitive impairment ameliorated by aiTBS.
Effect of GABAa-receptors on neuronal discharge and ion activity in focal seizures
Authors: Li Xin , Qu Zhongjie , Li Zipeng , Su Rui , Yin Bowen , Yin Liyong 
Published: March 2024

Abstract
Focal seizures are a type of epileptic event that has plagued the medical community for a long time, and the existing drug treatment is mainly based on the modulation of ${GABA}_a$-receptors to affect GABAergic signaling to achieve the therapeutic purpose. The majority of research currently focuses on the impact of ${GABA}_a$-receptors on neuronal firing, failing to analyze the molecular and ionic mechanisms involved. Specifically, the research on deeper-level mechanisms on how ${GABA}_a$-receptors affect neuronal firing by altering ion activity has not been addressed. This research aimed to study the effects of different ${GABA}_a$-receptor structures on ion activity in focal seizures model by adjusting parameters of the ${GABA}_a$-receptors: the rise time constant (${tau}_1$) and decay time constant (${tau}_2$). The research indicates that as the values of ${tau}_1$ and ${tau}_2$ of the ${GABA}_a$-receptor change, the ion concentration will vary based on the change of the ${GABA}_a$-receptor potential. To a certain extent, the duration of epileptic activity will also be affected to a certain extent. In conclusion, the alteration of ${GABA}_a$-receptor structure will affect the inhibitory effect of interneurons on pyramidal neurons, and different parameters of the ${GABA}_a$-receptor will directly impact the therapeutic effect.
Sex differences in fetal brain functional network topology
Authors: Liu Jing , Zhang Yujin , Jia Fenglin , Zhang Hongding , Luo Lekai , Liao Yi , Ouyang Minglei , Yi Xiaoxue , Zhu Ruixi , Bai Wanjing , Ning Gang , Li Xuesheng , Qu Haibo 
Published: March 2024

Abstract
The fetal period is a critical stage in brain development, and understanding the characteristics of the fetal brain is crucial. Although some studies have explored aspects of fetal brain functional networks, few have specifically focused on sex differences in brain network characteristics. We adopted the graph theory method to calculate brain network functional connectivity and topology properties (including global and nodal properties), and further compared the differences in these parameters between male and female fetuses. We found that male fetuses showed an increased clustering coefficient and local efficiency than female fetuses, but no significant group differences concerning other graph parameters and the functional connectivity matrix. Our study suggests the existence of sex-related distinctions in the topological properties of the brain network at the fetal stage of development and demonstrates an increase in brain network separation in male fetuses compared with female fetuses.
Group membership modulates empathic neural responses to pain in deaf individuals
Authors: Wu Xiangci , Jia Huibin , Zhao Kaibin , Guo Mengshan , Lv Xueqi , Ma Yimin , Wang Enguo 
Published: March 2024

Abstract
Empathy deficiencies are prevalent among deaf individuals. It has yet to be determined whether they exhibit an ingroup bias in empathic responses. This study employed explicit and implicit empathy tasks (i.e. attention-to-pain-cue [A-P] task and attention-to-nonpain-cue [A-N] task) to explore the temporal dynamics of neural activities when deaf individuals were processing painful/nonpainful stimuli from both ingroup models (deaf people) and outgroup models (hearing people), which aims to not only assist deaf individuals in gaining a deeper understanding of their intergroup empathy traits but also to aid in the advancement of inclusive education. In the A-P task, we found that (i) ingroup priming accelerated the response speed to painful/nonpainful pictures; (ii) the N2 amplitude of painful pictures was significantly more negative than that of nonpainful pictures in outgroup priming trials, whereas the N2 amplitude difference between painful and nonpainful pictures was not significant in ingroup priming trials. For N1 amplitude of the A-N task, we have similar findings. However, this pattern was reversed for P3/late positive component amplitude of the A-P task. These results suggest that the deaf individuals had difficulty in judging whether hearing individuals were in pain. However, their group identification and affective responses could shape the relatively early stage of pain empathy.
Evaluation of Factors Contributing to Diagnosis of Crohn’s Disease in the Face of Increasing Trend in Pakistan
Authors: Akhtar Tayyab Saeed , Ashraf Bilal , Zahid Kanza , Abbas Sameen , Sana Anosh , Khan Abdul Rauf , Ijaz Faiqa , Riaz Faisal 
Published: March 2024

Abstract

Background
Crohn’s disease (CD) is characterized by granulomatous inflammation of the digestive tract. Diagnosing CD involves assessing clinical symptoms, radiological and endoscopic findings, and histopathological evidence. Although previously considered a disease in developed countries, CD is increasing in developing nations, but challenges exist in diagnosing CD promptly. This study aims to report diagnostic parameters for early and correct CD diagnosis in Pakistan.

Methodology
A retrospective analysis from June 2016 to August 2023 of 22 CD patients was done, by data from medical records, questionnaires completed at diagnosis, and telephonic interviews. Baseline demographic and clinical characteristics were assessed, and patients were categorized using the Montreal classification.

Results
CD was diagnosed in 22 patients, with a 1:1 male-to-female ratio with a mean age of 33 years (range 15–55 years). Symptoms at presentation included abdominal pain (95.5%), watery diarrhea (86.4%), fever (31.8%), rectal bleeding (54.5%), and weight loss (81.8%) with 68% having symptoms for over 12 months before diagnosis. Disease characteristics were diverse, with various patterns of involvement and histopathological findings.

Conclusions
In resource-limited countries like Pakistan, the timely diagnosis of CD presents a significant healthcare challenge. Therefore, it is necessary to tackle these complex problems by enhancing diagnostic capabilities, raising medical awareness, and improving access to healthcare resources.
Development and external validation of a dynamic risk score for early prediction of cardiogenic shock in cardiac intensive care units using machine learning
Authors: Hu Yuxuan , Lui Albert , Goldstein Mark , Sudarshan Mukund , Tinsay Andrea , Tsui Cindy , Maidman Samuel D , Medamana John , Jethani Neil , Puli Aahlad , Nguy Vuthy , Aphinyanaphongs Yindalon , Kiefer Nicholas , Smilowitz Nathaniel R , Horowitz James , Ahuja Tania , Fishman Glenn I , Hochman Judith , Katz Stuart , Bernard Samuel , Ranganath Rajesh 
Published: March 2024

Abstract

Aims
Myocardial infarction and heart failure are major cardiovascular diseases that affect millions of people in the USA with morbidity and mortality being highest among patients who develop cardiogenic shock. Early recognition of cardiogenic shock allows prompt implementation of treatment measures. Our objective is to develop a new dynamic risk score, called CShock, to improve early detection of cardiogenic shock in the cardiac intensive care unit (ICU).

Methods and results
We developed and externally validated a deep learning-based risk stratification tool, called CShock, for patients admitted into the cardiac ICU with acute decompensated heart failure and/or myocardial infarction to predict the onset of cardiogenic shock. We prepared a cardiac ICU dataset using the Medical Information Mart for Intensive Care-III database by annotating with physician-adjudicated outcomes. This dataset which consisted of 1500 patients with 204 having cardiogenic/mixed shock was then used to train CShock. The features used to train the model for CShock included patient demographics, cardiac ICU admission diagnoses, routinely measured laboratory values and vital signs, and relevant features manually extracted from echocardiogram and left heart catheterization reports. We externally validated the risk model on the New York University (NYU) Langone Health cardiac ICU database which was also annotated with physician-adjudicated outcomes. The external validation cohort consisted of 131 patients with 25 patients experiencing cardiogenic/mixed shock. CShock achieved an area under the receiver operator characteristic curve (AUROC) of 0.821 (95% CI 0.792–0.850). CShock was externally validated in the more contemporary NYU cohort and achieved an AUROC of 0.800 (95% CI 0.717–0.884), demonstrating its generalizability in other cardiac ICUs. Having an elevated heart rate is most predictive of cardiogenic shock development based on Shapley values. The other top 10 predictors are having an admission diagnosis of myocardial infarction with ST-segment elevation, having an admission diagnosis of acute decompensated heart failure, Braden Scale, Glasgow Coma Scale, blood urea nitrogen, systolic blood pressure, serum chloride, serum sodium, and arterial blood pH.

Conclusion
The novel CShock score has the potential to provide automated detection and early warning for cardiogenic shock and improve the outcomes for millions of patients who suffer from myocardial infarction and heart failure.
Stopover habitat selection drives variation in the gut microbiome composition and pathogen acquisition by migrating shorebirds
Authors: Włodarczyk Radosław , Drzewińska-Chańko Joanna , Kamiński Maciej , Meissner Włodzimierz , Rapczyński Jan , Janik-Superson Katarzyna , Krawczyk Dawid , Strapagiel Dominik , Ożarowska Agnieszka , Stępniewska Katarzyna , Minias Piotr 
Published: March 2024

AbstractLong-distance host movements play a major regulatory role in shaping microbial communities of their digestive tract. Here, we studied gut microbiota composition during seasonal migration in five shorebird species (Charadrii) that use different migratory (stopover) habitats. Our analyses revealed significant interspecific variation in both composition and diversity of gut microbiome, but the effect of host identity was weak. A strong variation in gut microbiota was observed between coastal and inland (dam reservoir and river valley) stopover habitats within species. Comparisons between host age classes provided support for an increasing alpha diversity of gut microbiota during ontogeny and an age-related remodeling of microbiome composition. There was, however, no correlation between microbiome and diet composition across study species. Finally, we detected high prevalence of avian pathogens, which may cause zoonotic diseases in humans (e.g. Vibrio cholerae) and we identified stopover habitat as one of the major axes of variation in the bacterial pathogen exposure risk in shorebirds. Our study not only sheds new light on ecological processes that shape avian gut microbiota, but also has implications for our better understanding of host–pathogen interface and the role of birds in long-distance transmission of pathogens.
Prefire Drought Intensity Drives Postfire Recovery and Mortality in Pinus monticola and Pseudotsuga menziesii Saplings
Authors: Sparks Aaron M , Blanco Alexander S , Lad Lauren E , Smith Alistair M S , Adams Henry D , Tinkham Wade T 
Published: March 2024

Abstract
Increasing frequency of droughts and wildfire are sparking concerns that these compounded disturbance events are pushing forested ecosystems beyond recovery. An improved understanding of how compounded events affect tree physiology and mortality is needed given the reliance of fire management planning on accurate estimates of postfire tree mortality. In this study, we use a toxicological dose-response approach to quantify the impact of variable-intensity drought and fire on the physiology and mortality of Pinus monticola and Pseudotsuga menziesii saplings. We show that the dose-response relationship between fire intensity and mortality shifts toward increased vulnerability under drought, indicating higher mortality with increasing drought at any fire intensity. The trajectory we observed in postfire chlorophyll fluorescence, an indicator of photosynthetic efficiency and stress, was an effective early warning sign of impending tree death. Postfire mortality modeling shows that accurate mortality classification can be achieved using prefire physiology and morphology metrics combined with fire intensity. Variable importance measures indicate that physiological condition and fire intensity have greater influence on the classification accuracy than morphological metrics. The wide range in drought and fire responses observed between this study and others highlights the need for more research on compound disturbance effects.
Study Implications: An improved understanding of how drought and fire affect tree physiology and mortality is needed by natural resource managers looking to predict postfire tree mortality. This study advances our compound disturbance understanding by subjecting conifer saplings to variable drought and fire intensities and quantifying and modeling moderate-term recovery and mortality. The results show reduced physiological recovery and amplified mortality in saplings exposed to greater drought and fire intensity. Overall, this study highlights the importance of physiological condition when modeling tree mortality and could potentially be used to inform current postfire tree mortality models.
Local uncertainty quantification for 3-D time-domain full-waveform inversion with ensemble Kalman filters: application to a North Sea OBC data set
Authors: Hoffmann Alexandre , Brossier Romain , Métivier Ludovic , Tarayoun Alizia 
Published: March 2024

SUMMARY
Full-waveform inversion (FWI) has emerged as the state-of-the art high resolution seismic imaging technique, both in seismology for global and regional scale imaging and in the industry for exploration purposes. While gaining in popularity, FWI, at an operational level, remains a heavy computational process involving the repeated solution of large-scale 3-D wave propagation problems. For this reason it is a common practice to focus the interpretation of the results on the final estimated model. This is forgetting FWI is an ill-posed inverse problem in a high dimensional space for which the solution is intrinsically non-unique. This is the reason why being able to qualify and quantify the uncertainty attached to a model estimated by FWI is key. To this end, we propose to extend at an operational level the concepts introduced in a previous study related to the coupling between ensemble Kalman filters (EnKFs) and FWI. These concepts had been developed for 2-D frequency-domain FWI. We extend it here to the case of 3-D time-domain FWI, relying on a source subsampling strategy to assimilate progressively the data within the Kalman filter. We apply our strategy to an ocean bottom cable field data set from the North Sea to illustrate its feasibility. We explore the convergence of the filter in terms of number of elements, and extract variance and covariance information showing which part of the model are well constrained and which are not. Analysing the variance helps to gain insight on how well the final estimated model is constrained by the whole FWI workflow. The variance maps appears as the superposition of a smooth trend related to the geometrical spreading and a high resolution trend related to reflectors. Mapping lines of the covariance (or correlation matrix) to the model space helps to gain insight on the local resolution. Through a wave propagation analysis, we are also able to relate variance peaks in the model space to variance peaks in the data space. Compared to other posterior-covariance approximation scheme, our combination between EnKF and FWI is intrinsically scalable, making it a good candidate for exploiting the recent exascale high performance computing machines.
Incorporating topographic effects in surface wave tomography based on shortest-path ray tracing
Authors: Du Nanqiao , Yang Tingwei , Xu Tao , Liu Qinya 
Published: March 2024

SUMMARY
We propose a new method to obtain 3-D shear wave velocity structures for regions with undulating topography based on surface wave dispersion data. In our method, we assume that surface waves propagate along a curved free surface, and the dispersion effects of these waves can be modelled as a frequency-dependent ray tracing problem. We use the shortest-path method to address off-great-circle propagation arising from inhomogeneous elastic parameters and topographic variations. We then apply our method to both synthetic and real-data inversions and demonstrate that ignoring topographic effects may significantly distort the inverted images. Finally, we analysed the accuracy of our method and provided a rule-of-thumb principle to quantitatively assess the need to account for topographic effects in surface wave tomography for a region.
Prognosis-based management of unexplained infertility—why not?
Authors: Shingshetty Laxmi , Wang Rui , Feng Qian , Maheshwari Abha , Mol Ben W 
Published: March 2024

Abstract
Up to a half of couples seeking medical assistance for infertility are diagnosed with unexplained infertility, characterized by normal ovulation, tubal patency, and semen analysis results. This condition presents a challenge in determining the optimal treatment approach. Available treatments include IUI and IVF, but guidelines vary on when to offer each. Prognosis-based management is identified as a research priority, and various prediction models have been developed to guide treatment decisions. Prognostic factors include female age, duration of subfertility, and sperm parameters, among others. Prognosis-based strategies can enhance cost-effectiveness, safety, and patient outcomes, offering less invasive options to those with good prognoses and more aggressive interventions to those with poor prognoses. However, there is a gap between research evidence and its clinical application. In this article, we discuss the application of prognosis-based management in the context of unexplained infertility, highlighting its potential to improve clinical decision-making and patient outcomes.