bluuebunny/crossref_metadata_2025_split huggingface dataset:
Oral dextrose gel for hypoglycemia in a well-baby nursery: a baby-friendly initiative
Authors: Batra Mansi , Ikeri Kelechi , Blake Michelle , Mantell Genevieve , Bhat Ramachandra , Zayek Michael 
Published: September 2024

Abstract
Objectives
To assess the impact of oral dextrose gel (ODG) treatment on NICU admission rates for hypoglycemic infants in a well-baby nursery.

Study design
We retrospectively compared newborn infants at risk for hypoglycemia born during the intervention period (n = 3775) with historical controls (n = 655). We also compared the rates of the primary outcome (NICU admission) and secondary outcomes (exclusive breastfeeding and hospital costs) between the two periods.

Results
Following the implementation of ODG supplementation, the NICU admissions rates dropped from 4% to 2%, p < 0.05, for at-risk infants and from 15% to 7%, p < 0.05, for hypoglycemic infants in the baseline and intervention periods, respectively, with an adjusted OR (95% CI) of 0.39 (0.24–0.64), p < 0.001. Additionally, the ODG protocol sustained rates of exclusive breastfeeding in contrast to the institutional protocol.

Conclusion
The adoption of an ODG protocol fosters a more nurturing and baby-friendly environment through reduced NICU transfers, support for exclusive breastfeeding, and decreased hospital costs.
Pharmacokinetic characteristics of mesenchymal stem cells in translational challenges
Authors: Shan Yunlong , Zhang Mengying , Tao Enxiang , Wang Jing , Wei Ning , Lu Yi , Liu Qing , Hao Kun , Zhou Fang , Wang Guangji 
Published: September 2024

AbstractOver the past two decades, mesenchymal stem/stromal cell (MSC) therapy has made substantial strides, transitioning from experimental clinical applications to commercial products. MSC therapies hold considerable promise for treating refractory and critical conditions such as acute graft-versus-host disease, amyotrophic lateral sclerosis, and acute respiratory distress syndrome. Despite recent successes in clinical and commercial applications, MSC therapy still faces challenges when used as a commercial product. Current detection methods have limitations, leaving the dynamic biodistribution, persistence in injured tissues, and ultimate fate of MSCs in patients unclear. Clarifying the relationship between the pharmacokinetic characteristics of MSCs and their therapeutic effects is crucial for patient stratification and the formulation of precise therapeutic regimens. Moreover, the development of advanced imaging and tracking technologies is essential to address these clinical challenges. This review provides a comprehensive analysis of the kinetic properties, key regulatory molecules, different fates, and detection methods relevant to MSCs and discusses concerns in evaluating MSC druggability from the perspective of integrating pharmacokinetics and efficacy. A better understanding of these challenges could improve MSC clinical efficacy and speed up the introduction of MSC therapy products to the market.
Text mining method to unravel long COVID’s clinical condition in hospitalized patients
Authors: Veras Florentino Pilar Tavares , Araújo Vinícius de Oliveira , Zatti Henrique , Luis Caio Vinícius , Cavalcanti Célia Regina Santos , de Oliveira Matheus Henrique Citibaldi , Leão Anderson Henrique França Figueredo , Bertoldo Junior Juracy , Barbosa George G. Caique , Ravera Ernesto , Cebukin Alberto , David Renata Bernardes , de Melo Danilo Batista Vieira , Machado Tales Mota , Bellei Nancy C. J. , Boaventura Viviane , Barral-Netto Manoel , Smaili Soraya S. 
Published: September 2024

AbstractLong COVID is characterized by persistent that extends symptoms beyond established timeframes. Its varied presentation across different populations and healthcare systems poses significant challenges in understanding its clinical manifestations and implications. In this study, we present a novel application of text mining technique to automatically extract unstructured data from a long COVID survey conducted at a prominent university hospital in São Paulo, Brazil. Our phonetic text clustering (PTC) method enables the exploration of unstructured Electronic Healthcare Records (EHR) data to unify different written forms of similar terms into a single phonemic representation. We used n-gram text analysis to detect compound words and negated terms in Portuguese-BR, focusing on medical conditions and symptoms related to long COVID. By leveraging text mining, we aim to contribute to a deeper understanding of this chronic condition and its implications for healthcare systems globally. The model developed in this study has the potential for scalability and applicability in other healthcare settings, thereby supporting broader research efforts and informing clinical decision-making for long COVID patients.
Unprecedented mechanical wave energy absorption observed in multifunctional bioinspired architected metamaterials
Authors: Li Zhendong , Wang Xinxin , Zeng Kexin , Guo Zichao , Li Chong , Yu Xiang , Ramakrishna Seeram , Wang Zhonggang , Lu Yang 
Published: September 2024

Abstract
In practical engineering, noise and impact hazards are pervasive, indicating the pressing demand for materials that can absorb both sound and stress wave energy simultaneously. However, the rational design of such multifunctional materials remains a challenge. Herein, inspired by cuttlebone, we present bioinspired architected metamaterials with unprecedented sound-absorbing and mechanical properties engineered via a weakly-coupled design. The acoustic elements feature heterogeneous multilayered resonators, whereas the mechanical responses are based on asymmetric cambered cell walls. These metamaterials experimentally demonstrated an average absorption coefficient of 0.80 from 1.0 to 6.0 kHz, with 77% of the data points exceeding the desired 0.75 threshold, all with a compact 21 mm thickness. An absorptance-thickness map is devised for assessing the sound-absorption efficiency. The high-fidelity microstructure-based model reveals the air friction damping mechanism, with broadband behavior attributed to multimodal hybrid resonance. Empowered by the cambered design of cell walls, metamaterials shift catastrophic failure toward a progressive deformation mode characterized by stable stress plateaus and ultrahigh specific energy absorption of 50.7 J/g—a 558.4% increase over the straight-wall design. After the deformation mechanisms are elucidated, a comprehensive research framework for burgeoning acousto-mechanical metamaterials is proposed. Overall, our study broadens the horizon for multifunctional material design.
Large-scale simulations of Floquet physics on near-term quantum computers
Authors: Eckstein Timo , Mansuroglu Refik , Czarnik Piotr , Zhu Jian-Xin , Hartmann Michael J. , Cincio Lukasz , Sornborger Andrew T. , Holmes Zoë 
Published: September 2024

AbstractPeriodically driven quantum systems exhibit a diverse set of phenomena but are more challenging to simulate than their equilibrium counterparts. Here, we introduce the Quantum High-Frequency Floquet Simulation (QHiFFS) algorithm as a method to simulate fast-driven quantum systems on quantum hardware. Central to QHiFFS is the concept of a kick operator which transforms the system into a basis where the dynamics is governed by a time-independent effective Hamiltonian. This allows prior methods for time-independent simulation to be lifted to simulate Floquet systems. We use the periodically driven biaxial next-nearest neighbor Ising (BNNNI) model, a natural test bed for quantum frustrated magnetism and criticality, as a case study to illustrate our algorithm. We implemented a 20-qubit simulation of the driven two-dimensional BNNNI model on Quantinuum’s trapped ion quantum computer. Our error analysis shows that QHiFFS exhibits not only a cubic advantage in driving frequency ω but also a linear advantage in simulation time t compared to Trotterization.
Quantum autoencoders using mixed reference states
Authors: Ma Hailan , Mooney Gary J. , Petersen Ian R. , Hollenberg Lloyd C. L. , Dong Daoyi 
Published: September 2024

AbstractOne of the fundamental tasks in quantum information theory is quantum data compression, which can be realized via quantum autoencoders that first compress quantum states to low-dimensional ones and then recover to the original ones with a reference state. When taking a pure reference state, there exists an upper bound for the encoding fidelity, which limits the compression of states with high entropy. To overcome the entropy inconsistency, we allow the reference state to be a mixed state and propose a cost function that combines the encoding fidelity and the quantum mutual information. We consider the reference states to be a mixture of maximally mixed states and pure states and propose three strategies for setting the ratio of mixedness. Numerical simulations of different states and experimental implementations on IBM quantum computers illustrate the effectiveness of our approach.
COVID-19 vaccine refusal is driven by deliberate ignorance and cognitive distortions
Authors: Fuławka Kamil , Hertwig Ralph , Pachur Thorsten 
Published: September 2024

AbstractVaccine hesitancy was a major challenge during the COVID-19 pandemic. A common but sometimes ineffective intervention to reduce vaccine hesitancy involves providing information on vaccine effectiveness, side effects, and related probabilities. Could biased processing of this information contribute to vaccine refusal? We examined the information inspection of 1200 U.S. participants with anti-vaccination, neutral, or pro-vaccination attitudes before they stated their willingness to accept eight different COVID-19 vaccines. All participants—particularly those who were anti-vaccination—frequently ignored some of the information. This deliberate ignorance, especially toward probabilities of extreme side effects, was a stronger predictor of vaccine refusal than typically investigated demographic variables. Computational modeling suggested that vaccine refusals among anti-vaccination participants were driven by ignoring even inspected information. In the neutral and pro-vaccination groups, vaccine refusal was driven by distorted processing of side effects and their probabilities. Our findings highlight the necessity for interventions tailored to individual information-processing tendencies.
Incorporation of nitrogen in antinutritional Solanum alkaloid biosynthesis
Authors: Grzech Dagny , Smit Samuel J. , Alam Ryan M. , Boccia Marianna , Nakamura Yoko , Hong Benke , Barbole Ranjit , Heinicke Sarah , Kunert Maritta , Seibt Wibke , Grabe Veit , Caputi Lorenzo , Lichman Benjamin R. , O’Connor Sarah E. , Aharoni Asaph , Sonawane Prashant D. 
Published: September 2024

AbstractSteroidal glycoalkaloids (SGAs) are specialized metabolites produced by hundreds of Solanum species including food crops, such as tomato, potato and eggplant. Unlike true alkaloids, nitrogen is introduced at a late stage of SGA biosynthesis through an unknown transamination reaction. Here, we reveal the mechanism by which GLYCOALKALOID METABOLISM12 (GAME12) directs the biosynthesis of nitrogen-containing steroidal alkaloid aglycone in Solanum. We report that GAME12, a neofunctionalized γ-aminobutyric acid (GABA) transaminase, undergoes changes in both active site specificity and subcellular localization to switch from its renown and generic activity in core metabolism to function in a specialized metabolic pathway. Moreover, overexpression of GAME12 alone in engineered S. nigrum leaves is sufficient for de novo production of nitrogen-containing SGAs. Our results highlight how hijacking a core metabolism GABA shunt enzyme is crucial in numerous Solanum species for incorporating a nitrogen to a steroidal-specialized metabolite backbone and form defensive alkaloids.
A U-shaped association between selenium intake and cancer risk
Authors: Le Ngoan Tran , Pham Yen Thi-Hai , Le Chung Thi-Kim , Le Linh Thuy , Le Thanh-Do , Dao Hang Viet , Ha Toan H. , Kuchipudi Suresh V. , Luu Hung N. 
Published: September 2024

AbstractWhile selenium is a cofactor of several antioxidant enzymes against cancer and is essential for human health, its excess intake may also be harmful. Though a safe intake of selenium has recently been recommended, it is not well understood in the Asian population. We aimed to determine the association between dietary intake of selenium and cancer risk in a case–control study of 3758 incident cancer cases (i.e., stomach, colon, rectum, lung cancers, and other sites) and 2929 control subjects in Vietnam. Daily intake of selenium was derived from a semiquantitative food frequency questionnaire. The unconditional logistic regression model was used to calculate the odds ratios (ORs) and 95% confidence intervals (CIs) for the association between selenium intake and cancer risk. We observed a U-shaped association between selenium intake and cancer risk. A safe intake ranged from 110.8 to 124.4 µg/day (mean 117.8 µg/day). Compared to individuals with the safe intake of selenium, individuals with the lowest intake (i.e., 27.8–77.2 µg/day) were associated with an increased risk of cancer (OR = 3.78, 95% CI 2.89–4.95) and those with the highest intake (169.1–331.7 µg/day) also had an increased cancer risk (OR = 1.86, 95% CI 1.45–2.39). A U-shaped pattern of association between selenium intake and cancer risk was stronger among participants with body mass index (BMI) < 23 kg/m2 and never smokers than BMI ≥ 23 kg/m2 and ever smokers (P’sheterogeneity = 0.003 and 0.021, respectively) but found in both never and ever-drinkers of alcohol (Pheterogeneity = 0.001). A U-shaped association between selenium intake and cancer risk was seen in cancer sites of the stomach, colon, rectum, and lung cancers. In summary, we found a U-shaped association between selenium intake and cancer risk and a safe selenium intake (mean: 117.8 µg/day) in the Vietnamese population. Further mechanistic investigation is warranted to understand better a U-shaped association between selenium intake and cancer risk.
Children’s limited tooling ability in a novel concurrent tool use task supports the innovation gap
Authors: Colbourne Jennifer A. D. , Auersperg Alice M. I. , Beck Sarah R. 
Published: September 2024

AbstractSchool-aged children have consistently shown a surprising developmental lag when attempting to innovate solutions to tool use tasks, despite being capable of learning to solve these problems from a demonstrator. We suggest that this “innovation gap” arises from tool tasks with more complex spatial relations. Following Fragaszy and Mangalam’s new tooling theory, we predicted that innovating a new “sticker slide” task should be more challenging when two tools need to be used at the same time (concurrently) rather than one at a time (sequentially), despite the similarity of the other task elements. In line with previous work, both versions of the task were challenging for all ages of children (4–9 years) that we tested. However, the youngest group showed particularly extreme difficulties, which was marked by not a single child innovating the concurrent version. Although success significantly increased with age, even the oldest group failed to reach 50% success on the concurrent version of the task, whereas the majority of the two older groups could solve the sequential version. Thus, in this first study of concurrent tool use in children, we found support for the prediction that increasing the complexity of spatial relations in tooling exacerbates the innovation gap.
Large language models predict human sensory judgments across six modalities
Authors: Marjieh Raja , Sucholutsky Ilia , van Rijn Pol , Jacoby Nori , Griffiths Thomas L. 
Published: September 2024

AbstractDetermining the extent to which the perceptual world can be recovered from language is a longstanding problem in philosophy and cognitive science. We show that state-of-the-art large language models can unlock new insights into this problem by providing a lower bound on the amount of perceptual information that can be extracted from language. Specifically, we elicit pairwise similarity judgments from GPT models across six psychophysical datasets. We show that the judgments are significantly correlated with human data across all domains, recovering well-known representations like the color wheel and pitch spiral. Surprisingly, we find that a model (GPT-4) co-trained on vision and language does not necessarily lead to improvements specific to the visual modality, and provides highly correlated predictions with human data irrespective of whether direct visual input is provided or purely textual descriptors. To study the impact of specific languages, we also apply the models to a multilingual color-naming task. We find that GPT-4 replicates cross-linguistic variation in English and Russian illuminating the interaction of language and perception.
Engagement analysis of a persuasive-design-optimized eHealth intervention through machine learning
Authors: Idrees Abdul Rahman , Beierle Felix , Mutter Agnes , Kraft Robin , Garatva Patricia , Baumeister Harald , Reichert Manfred , Pryss Rüdiger 
Published: September 2024

AbstractThe challenge of sustaining user engagement in eHealth interventions is a pressing issue with significant implications for the effectiveness of these digital health tools. This study investigates user engagement in a cognitive-behavioral therapy-based eHealth intervention for procrastination, using a dataset from a randomized controlled trial of 233 university students. Various machine learning models, including Decision Tree, Gradient Boosting, Logistic Regression, Random Forest, and Support Vector Machines, were employed to predict patterns of user engagement. The study adopted a two-phase analytical approach. In the first phase, all features of the dataset were included, revealing ‘total_minutes’—the total time participants spent on the intervention and the eHealth platform—as the most significant predictor of engagement. This finding emphasizes the intuitive notion that early time spent on the platform and the intervention is a strong indicator of later user engagement. However, to gain a deeper understanding of engagement beyond this predominant metric, the second phase of the analysis excluded ‘total_minutes’. This approach allowed for the exploration of the roles and interdependencies of other engagement indicators, such as ‘number_intervention_answersheets’—the number of completed lessons, ‘logins_first_4_weeks’—login frequency, and ‘number_diary_answersheets’—the number of completed diaries. The results from this phase highlighted the multifaceted nature of engagement, showing that while ‘total_minutes’ is strongly correlated with engagement, indicating that more engaged participants tend to spend more time on the intervention, the comprehensive engagement profile also depends on additional aspects like lesson completions and frequency of platform interactions.
Design of a high voltage gain converter using coupled inductor with reduced voltage stress for photovoltaic energy based systems
Authors: Hashemzadeh Seyed Majid , Hosseini Seyed Hossein 
Published: September 2024

AbstractThis paper presents the design and analysis of a high voltage gain converter utilizing a coupled inductor with reduced voltage stress, specifically for photovoltaic energy-based systems. The proposed converter employs a two-winding coupled inductor and voltage multiplier cells to achieve an increase in output voltage while mitigating voltage stress across semiconductor components. Additionally, the voltage multiplier cells function as voltage clamps for the power switch, further enhancing the converter's performance. The converter features a single switch design, which simplifies control, reduces cost, and improves reliability. Key advantages of the converter include a low component count, a common ground between input and output ports, and high efficiency. The converter's performance is thoroughly investigated through mode analysis and steady-state analysis. Comparative evaluations with similar converters are conducted to highlight the benefits and performance of the proposed design. To validate the theoretical analysis, a 125 W prototype with 26 V input and 200 V output voltages operating at a 50 kHz switching frequency is developed, and experimental results are presented, demonstrating the effectiveness and practicality of the proposed high voltage gain converter.
Global patterns of plant functional traits and their relationships to climate
Authors: Li Jiaze , Prentice Iain Colin 
Published: September 2024

AbstractPlant functional traits (FTs) determine growth, reproduction and survival strategies of plants adapted to their growth environment. Exploring global geographic patterns of FTs, their covariation and their relationships to climate are necessary steps towards better-founded predictions of how global environmental change will affect ecosystem composition. We compile an extensive global dataset for 16 FTs and characterise trait-trait and trait-climate relationships separately within non-woody, woody deciduous and woody evergreen plant groups, using multivariate analysis and generalised additive models (GAMs). Among the six major FTs considered, two dominant trait dimensions—representing plant size and the leaf economics spectrum (LES) respectively—are identified within all three groups. Size traits (plant height, diaspore mass) however are generally higher in warmer climates, while LES traits (leaf mass and nitrogen per area) are higher in drier climates. Larger leaves are associated principally with warmer winters in woody evergreens, but with wetter climates in non-woody plants. GAM-simulated global patterns for all 16 FTs explain up to three-quarters of global trait variation. Global maps obtained by upscaling GAMs are broadly in agreement with iNaturalist citizen-science FT data. This analysis contributes to the foundations for global trait-based ecosystem modelling by demonstrating universal relationships between FTs and climate.
RUNX1 interacts with lncRNA SMANTIS to regulate monocytic cell functions
Authors: Weiss Lisa M. , Warwick Timothy , Zehr Simonida , Günther Stefan , Wolf Sebastian , Schmachtel Tessa , Izquierdo Ponce Judit , Pálfi Katalin , Teichmann Tom , Schneider Alicia , Stötzel Julia , Knapp Stefan , Weigert Andreas , Savai Rajkumar , Rieger Michael A. , Oellerich Thomas , Wittig Ilka , Oo James A. , Brandes Ralf P. , Leisegang Matthias S. 
Published: September 2024

AbstractMonocytes, the circulating macrophage precursors, contribute to diseases like atherosclerosis and asthma. Long non-coding RNAs (lncRNAs) have been shown to modulate the phenotype and inflammatory capacity of monocytes. We previously discovered the lncRNA SMANTIS, which contributes to cellular phenotype expression by controlling BRG1 in mesenchymal cells. Here, we report that SMANTIS is particularly highly expressed in monocytes and lost during differentiation into macrophages. Moreover, different types of myeloid leukemia presented specific SMANTIS expression patterns. Interaction studies revealed that SMANTIS binds RUNX1, a transcription factor frequently mutated in AML, primarily through its Alu-element on the RUNT domain. RNA-seq after CRISPR/Cas9-mediated deletion of SMANTIS or RUNX1 revealed an association with cell adhesion and both limited the monocyte adhesion to endothelial cells. Mechanistically, SMANTIS KO reduced RUNX1 genomic binding and altered the interaction of RUNX1 with EP300 and CBFB. Collectively, SMANTIS interacts with RUNX1 and attenuates monocyte adhesion, which might limit monocyte vascular egress.