bluuebunny/crossref_metadata_2025_split huggingface dataset:
Mental health impacts of spousal caregiving intensity in the US
Authors: Ailshire Jennifer A. , Casanova Maria 
Published: October 2024

AbstractIn the US, spouses are a major source of informal care for older adults and, therefore, key to the US national strategy to provide long‐term care to the growing population of older adults. Understanding the mental health impacts of spousal caregiving is therefore critically important. Existing studies on the topic have often been limited by methodological limitations, and most overlook the role of caregiving intensity. In this study, we assess the impact of providing different intensities of caregiving to a spouse on mental health outcomes using data from the Health and Retirement Study. We address the endogeneity of the decision to provide different caregiving intensities using an augmented inverse probability weighted (AIPW) estimator adapted to handle multivalued treatments. We check the robustness of our estimates to the AIPW's identifying assumptions by re‐estimating the model using a dynamic fixed effects estimator. We find strong evidence that becoming a caregiver is associated with worsened mental health outcomes for women, while the evidence for men is weaker. When disaggregating by caregiving intensity, we find that transitions into high‐intensity caregiving lead to large increases in depression symptoms and the probability of exhibiting major depression for both men and women. The overall mental health impact of transiting into caregiving is driven almost exclusively by the negative impact of high‐intensity caregiving, as transitions into low‐ and moderate‐intensity caregiving are not associated with worsening mental health in most specifications. This study provides timely insights that can inform the targeting of long‐term care policies and programs aimed at supporting family caregivers of older adults.
The power of artificial intelligence for managing pandemics: A primer for public health professionals
Authors: McKee Martin , Rosenbacke Rikard , Stuckler David 
Published: October 2024

AbstractArtificial intelligence (AI) applications are complex and rapidly evolving, and thus often poorly understood, but have potentially profound implications for public health. We offer a primer for public health professionals that explains some of the key concepts involved and examines how these applications might be used in the response to a future pandemic. They include early outbreak detection, predictive modelling, healthcare management, risk communication, and health surveillance. Artificial intelligence applications, especially predictive algorithms, have the ability to anticipate outbreaks by integrating diverse datasets such as social media, meteorological data, and mobile phone movement data. Artificial intelligence‐powered tools can also optimise healthcare delivery by managing the allocation of resources and reducing healthcare workers' exposure to risks. In resource distribution, they can anticipate demand and optimise logistics, while AI‐driven robots can minimise physical contact in healthcare settings. Artificial intelligence also shows promise in supporting public health decision‐making by simulating the social and economic impacts of different policy interventions. These simulations help policymakers evaluate complex scenarios such as lockdowns and resource allocation. Additionally, it can enhance public health messaging, with AI‐generated health communications shown to be more effective than human‐generated messages in some cases. However, there are risks, such as privacy concerns, biases in models, and the potential for ‘false confirmations’, where AI reinforces incorrect decisions. Despite these challenges, we argue that AI will become increasingly important in public health crises, but only if integrated thoughtfully into existing systems and processes.
A Novel Dictionary Learning Algorithm Based on Prior Knowledge for fMRI Data Analysis
Authors: Sheng Fangmin , Shi Yuhu , Wang Lei , Li Ying , Zhang Hua , Zeng Weiming 
Published: October 2024

ABSTRACTTask‐based functional magnetic resonance imaging (fMRI) has been widely utilized for brain activation detection and functional network analysis. In recent years, the K‐singular value decomposition (K‐SVD) algorithm has gained increasing attention in the research of fMRI data analysis methods. In this study, we propose a novel temporal feature region‐growing constrained K‐SVD algorithm that incorporates task‐based fMRI temporal prior knowledge and utilizes a region‐growing algorithm to infer potential activation locations. The algorithm incorporates temporal and spatial constraints to enhance the detection of brain activation. Specifically, this paper improves the three stages of the traditional K‐SVD algorithm. First, in the dictionary initialization stage, the automatic target generation process with an independent component analysis algorithm is utilized in conjunction with prior knowledge to enhance the accuracy of initialization. Second, in the sparse coding stage, the region‐growing algorithm is employed to infer potential activation locations based on temporal prior knowledge, thereby imposing spatial constraints to limit the extent of activation regions. Finally, in the dictionary learning stage, soft constraints and low correlation constraints are applied to reinforce the consistency with prior knowledge and enhance the robustness of learning for task‐related atoms. The proposed method was validated on simulated and real fMRI data, showing superior performance in detecting brain activation compared with traditional methods. The results indicate that the algorithm accurately identifies activated brain regions, providing an effective approach for studying brain function in clinical applications.
Humanizing pedagogies and student‐centered instruction in a networked improvement community
Authors: Nguyen JoeAnn , Schunn Christian D. 
Published: October 2024

AbstractImproved English Language Arts instruction within classrooms and schools that typically serve low‐income, English Language Learners, and Students with Special Needs is important for ensuring all students receive rigorous and inclusive instruction. From the case of a Networked Improvement Community focused on improving instructional practices through student‐centered routines, this research explores the role of humanizing pedagogies in enabling effective student‐centered instructional routines by interviewing 16 teachers who were identified as particularly successful in working with Students of Color. Two major themes and six subthemes were identified from these interviews focused on building relationships as a foundation for humanizing pedagogies and then leveraging those relationships to create a positive classroom culture that fosters increased student engagement and academic risk‐taking with challenging student‐centered routines. These strong teacher–student relationships and student‐centered routines led to improved academic outcomes.
Efficient treatment of printing and dyeing reverse osmosis concentrate by a Ti‐NTA/SnO2‐Sb2O3 electrocatalytic membrane
Authors: Li Chengjie , Mo Yinghui , Wang Liang , Guo Heng , Ji Xiaoxue 
Published: October 2024

AbstractBACKGROUNDPrinting and dyeing reverse osmosis concentrates (ROC) contain large amounts of salts and organic matter, which is a major environmental issue. The unique composition of such wastewater makes the use of traditional physicochemical techniques challenging.RESULTSIn this paper, titanium dioxide nanotube arrays/SnO2‐Sb2O3 (Ti‐NTA/SnO2‐Sb2O3) electrodes were prepared by anodizing–cathodizing and sol–gel method for the electrocatalytic degradation of printing and dyeing ROC. Organic matter removal under different situations was examined, and the best treatment parameters were identified.CONCLUSIONFollowing treatment, organic matter concentration dropped from 1600 to 50 mg L−1, or even less. The outstanding removal performance of the Ti‐NTA/SnO2‐Sb2O3 electrode was validated by full‐scan ultraviolet spectra, gas chromatography–mass spectrometry and three‐dimensional fluorescence. Evaluation of the dissolved heavy metals and halogenated degradation process byproducts revealed that the electrocatalytic membranes were environmentally safe. These findings highlight the enormous potential of electrocatalytic membranes for the treatment of high‐salt ROC when equipped with a Ti‐NTA/SnO2‐Sb2O3 electrode. © 2024 Society of Chemical Industry (SCI).
Klippel‐Trénaunay‐Weber Syndrome: Prenatal Diagnosis and Review of the Literature
Authors: Orlandi Giuliana , Sarno Laura , Angelino Antonio , Motta Mariarosaria , Di Girolamo Raffaella , Carbone Luigi , Rovetto Marika , Mazzarelli Laura Letizia , Sglavo Gabriella , D' Antonio Francesco , Mappa Ilenia , Di Mascio Daniele , Rizzo Giuseppe , Maria Maruotti Giuseppe 
Published: October 2024

ABSTRACTKlippel‐Trénaunay‐Weber syndrome (KTW) is a rare congenital disease, representing a challenge in prenatal diagnosis due to overlapping characteristics with other syndromes and no specific genetic markers known to date. We have collected all the cases present in the literature on the prenatal diagnosis of KTW, emphasizing common ultrasound findings that can guide the clinician and genetics to the prenatal counseling. Thus, we collected all the information about the postnatal prognosis and the necessity for treatment. Our review of 44 cases highlights the typical common features: hemihypertrophy, predominantly affecting the right leg, with cystic lesions extending to the trunk or upper limbs and rare internal organ involvement. Prenatal complications, including hydrops and polyhydramnios, emphasize the need for a careful ultrasound follow‐up. Despite no identified genetic mutation, genetic counseling and invasive testing are recommended. Mortality rate due to a severe complication known as Kasabach‐Merritt syndrome, underlines the importance of early diagnosis and accurate management strategies. Prenatal diagnosis of KTW, guided by ultrasound findings and genetic counseling, could help with informed decision‐making and optimal care planning.
Comparative Analysis of 2022 Outbreak MPXV and Previous Clade II MPXV
Authors: McGrail Joseph Patrick , Mondolfi Alberto Paniz , Ramírez Juan David , Vidal Santiago , García‐Sastre Adolfo , Palacios Gustavo , Sanchez‐Seco Mari Paz , Guerra Susana 
Published: October 2024

ABSTRACTThe 2022–2024 outbreak of MPOX is an important worldwide public health issue that has triggered significant concerns in the scientific community. MPOX is caused by monkeypox virus (MPXV) belonging to the Poxviridae family. The study of MPXV presents a multifaceted challenge due to the diverse viral formThis study was supported by ISIDORe consortium and Agencia Estatal de Investigación.s produced by this pathogen. Notably the intracellular mature viruses (MVs) primarily contribute to localized lesions and host‐to‐host transmission, while the extracellular enveloped viruses (EVs) are associated with systemic infection. Clinically, MPOX manifests as a vesiculopustular rash that initially emerges on the face and trunk, subsequently spreading throughout the body, with heightened severity observed in immunocompromised individuals. Results obtained in this manuscript indicate that the 2022 outbreak MPXV has a significantly slower viral cycle compared with previous Clade II strains, with WRAIR 7‐61 being more intermediate and USA 2003 producing highest viral titers. Additionally, proteomic and phospho‐proteomic analysis displays differences in protein expression between these three strains. These findings highlight key differences between the current Lineage B.1 MPXV and previous strains. Further studies will be undertaken to demonstrate if these differences are important for the apparent increased human‐to‐human transmission mechanisms observed in the Clade IIb MPXV outbreak.
Arctic sea‐ice export as a mechanism for cold climate events during the last deglaciation
Authors: Renssen Hans , Roche Didier M. 
Published: October 2024

ABSTRACTRecently it was proposed that a sudden drainage of accumulated Arctic sea ice could have caused cold climate events during the last deglaciation. To explore this mechanism, we performed numerical experiments with an atmosphere–ocean–sea ice model. In these experiments, the impact of a large flush of Arctic sea ice was compared to a reference glacial state of the climate. In our results, the sea ice flush produces a major surface freshening of the North Atlantic Ocean and a 17% weakening of the strength of the Atlantic Meridional Overturning Circulation. Together with an increase in surface albedo, this weakening in ocean circulation leads to a cooling over the North Atlantic, extending to the downwind continents. Compared to our reference glacial state, the cooling reaches 5°C and lasts about 80 years. This climate anomaly is similar in magnitude and duration to relatively short cooling events during the last deglaciation, such as the Older Dryas, the Inter‐Allerød cold period and the Preboreal Oscillation. We thus conclude that the sea‐ice flush mechanism is consistent with the occurrence of such cooling events in the North Atlantic region. However, longer cooling events such as the Younger Dryas would require additional mechanisms.
A posteriori error estimation and adaptivity for temporal multiscale problems
Authors: Lautsch Leopold , Richter Thomas 
Published: October 2024

AbstractIn science and engineering, problems over multiple scales in time often arise. Two examples are material damage in oscillating structures or plaque growth in pulsating blood vessels. Here the long term effects are of interest but they depend on the coupled fast‐changing physical processes which must be taken into account. One method to efficiently simulate these problems is to split them into an averaged slow scale process and a localized fast scale process. We apply the dual weighted residual method on a generalized formulation for this type of problem to obtain an error estimator which can be used for adaptive refinement. Special care must be taken to separate the slow scale and fast scale influences on the estimator.
Recording diagnostic conversations for communication research purposes in pediatric leukemia
Authors: Buursma Petra , Schepers Sasja A. , Kars Marijke C. , van den Bergh Esther M. M. , Dors Natasja , Grootenhuis Martha A. , Hoogerbrugge Peter M. 
Published: October 2024

AbstractRecordings of patient–doctor interactions is a recommended method in communication research. However, concerns are expressed regarding audio‐recording of conversations with vulnerable patients. Our study examined experiences of children, parents, and oncologists with recording diagnostic conversations in the pediatric acute leukemia setting. Results show that recording conversations is generally well received by virtually all children and parents. Pediatric oncologists seem to overestimate the expected emotional burden for children and parents, which may lead to gatekeeping by professionals. This in turn may lead to a decrease in patient autonomy and research quality when addressing relevant questions in communication science.
Flocculation potential regulation to achieve the improved thermal‐mechanical performance for CB/GO reinforced NR nanocomposites
Authors: Chen Jiaqi , Tian Dongsheng , Chen Hongfeng , Yu Huitao , Sun Zhijian , Zhang Zhiyi , Liu Yaqing , An Dong , Wong Chingping 
Published: October 2024

AbstractWith the rapid development of modern transportation systems, optimizing the relationship between structure and performance to obtain natural rubber‐based nanocomposites with excellent comprehensive performance is still worth to investigation. Herein, the regulation of flocculation potential through different flocculants selection on the thermomechanical properties for carbon black/graphene oxide reinforced natural rubber (CB/GO/NR) nanocomposites were investigated based on the compression electric double layer theory. The results showed that formic acid owned the highest zeta potential with comparison of sodium chloride (NaCl), calcium chloride (CaCl2), and aluminum chloride (AlCl3). Meanwhile, as the content was 8 wt%, tensile strength, tearing strength and thermal conductivity could achieve to 27.64 MPa, 56.89 N/mm, and 1.05 W m−1 K−1, respectively. While the heat generation after compression fatigue dropped to 10.1°C. These findings reveal that the highest zeta potential of formic acid could promote the improvement of flocculation degree between GO and NR latex and significantly enhanced the thermomechanical properties of CB/GO/NR nanocomposites due to the larger flocculation potential difference. Therefore, this study not only provides important theoretical insights for preparing high‐performance NR‐based nanocomposites, but also highlights the crucial role of high zeta potential flocculants in optimizing the composite performance. More importantly, the findings offer the creative insights for preparation of the excellent comprehensive NR nanocomposites for the practical industry application.Highlights
The effect of flocculation degree on the properties of CB/GO/NR composites was studied.
Formic acid was benefit to the flocculation and could promote the surface interaction.
CB/GO/NR composites possessed the improved thermal and mechanical properties.
Langerhans cell histiocytosis in an infant
Authors: Ye Diya , Yuan Tianming , Jiang Mizu 
Published: October 2024

AbstractA 2‐month‐old male infant presented with a persistent mild cough and a pink papule beneath the left eyelid. Imaging studies revealed diffuse lung opacities and a cystic shadow in the right middle lobe, with no evidence of bacterial or fungal infection. Skin biopsy demonstrated positive immunohistochemical staining for CD207/Langerin and CD1a, leading to a diagnosis of Langerhans‐cell histiocytosis (LCH). The infant's pulmonary bullae resolved following chemotherapy, and the patient is under surveillance for recurrence. LCH, recognized for its inflammatory and malignant characteristics, often presents with multisystemic involvement, including pulmonary manifestations. Timely diagnosis and treatment are crucial for managing this rare disorder in infants.
Infection and Inflammation in the Cystic fibrosis (CF) airway
Authors: Cohen‐Cymberknoh Malena 
Published: October 2024

AbstractCystic fibrosis (CF) airway disease is characterized by chronic infection and neutrophil‐driven inflammation, leading to progressive airway damage and early mortality. Infection with Pseudomonas aeruginosa significantly impacts morbidity and mortality, requiring early detection and aggressive antibiotic treatment. Controlling inflammation remains difficult due to the limited availability of effective anti‐inflammatory treatments. CFTR modulators show potential in reducing inflammation, but further research is needed. Effective management of CF lung disease requires a comprehensive approach addressing both infection and inflammation.
Unraveling the molecular determinants of a rare human mitochondrial disorder caused by the P144L mutation of FDX2
Authors: Grifagni Deborah , Doni Davide , Susini Bianca , Fonseca Bruno M. , Louro Ricardo O. , Costantini Paola , Ciofi‐Baffoni Simone 
Published: October 2024

AbstractEpisodic mitochondrial myopathy with or without optic atrophy and reversible leukoencephalopathy (MEOAL) is a rare, orphan autosomal recessive disorder caused by mutations in ferredoxin‐2 (FDX2), which is a [2Fe‐2S] cluster‐binding protein participating in the formation of iron–sulfur clusters in mitochondria. In this biosynthetic pathway, FDX2 works as electron donor to promote the assembly of both [2Fe‐2S] and [4Fe‐4S] clusters. A recently identified missense mutation of MEOAL is the homozygous mutation c.431C>T (p.P144L) described in six patients from two unrelated families. This mutation alters a highly conserved proline residue located in a loop of FDX2 that is distant from the [2Fe‐2S] cluster. How this Pro to Leu substitution damages iron–sulfur cluster biosynthesis is unknown. In this work, we have first compared the structural, dynamic, cluster binding and redox properties of WT and P144L [2Fe‐2S] FDX2 to have clues on how the pathogenic P144L mutation can perturb the FDX2 function. Then, we have investigated the interaction of both WT and P144L [2Fe‐2S] FDX2 with its physiological electron donor, ferredoxin reductase FDXR, comparing their electron transfer efficiency and protein–protein recognition patterns. Overall, the data indicate that the pathogenic P144L mutation negatively affects the FDXR‐dependent electron transfer pathway from NADPH to FDX2, thereby reducing the capacity of FDX2 in assembling both [2Fe‐2S] and [4Fe‐4S] clusters. Our study also provided solid molecular evidences on the functional role of the C‐terminal tail of FDX2 in the electron transfer between FDX2 and FDXR.
What to do with your maintenance records for protection layers?
Authors: Summers Angela E. , Bukowski Julia V. 
Published: October 2024

AbstractCollecting and analyzing operation and maintenance data is essential to process safety. The analysis aims to demonstrate that what is installed in the field achieves the required performance so that adjustments can be made if the required performance is not met. Very large organizations routinely have data collection systems and resources for data analysis. Midsized and smaller organizations collect the same data in various ways but often need help to analyze it. This paper presents examples of American Petroleum Institute (API) 754 Tier 3 and 4 metrics related to protection layers and how to use available plant data to support their analysis.
Within‐chain parallelization—Giving Stan Jet Fuel for population modeling in pharmacometrics
Authors: Davis Casey , Vaddady Pavan 
Published: October 2024

AbstractStan is a powerful probabilistic programming language designed mainly for Bayesian data analysis. Torsten is a collection of Stan functions that handles the events (e.g., dosing events) and solves the ODE systems that are frequently present in pharmacometric models. To perform a Bayesian data analysis, most models in pharmacometrics require Markov Chain Monte Carlo (MCMC) methods to sample from the posterior distribution. However, MCMC is computationally expensive and can be time‐consuming, enough so that people will often forgo Bayesian methods for a more traditional approach. This paper shows how to speed up the sampling process in Stan by within‐chain parallelization through both multi‐threading using Stan's reduce_sum() function and multi‐processing using Torsten's group ODE solver. Both methods show substantial reductions in the time necessary to sufficiently sample from the posterior distribution compared with a basic approach with no within‐chain parallelization.