bluuebunny/crossref_metadata_2025_split huggingface dataset:
Pricing strategies considering online reviews in a dual‐channel supply chain
Authors: Zhao Huan‐huan , Xu Jing‐yu 
Published: April 2024

AbstractAccompanied by the surge in online consumption, increasing online reviews exert more and more significant effects on pricing decisions, as well as the restructuring and profit redistribution of supply chains. To analyze the impact of online reviews, we develop a two‐stage model of the retailer's rebate strategy, examine how the retailer's rebate affects online reviews, the consumer's perceived utility, and the retailer's profits, and discuss the optional pricing strategies.
Dietary Supplementation with Nervonic Acid Ameliorates Cerebral Ischemia‐Reperfusion Injury by Modulating of Gut Microbiota Composition‐Fecal Metabolites Interaction
Authors: Zhou Hui , Chen Ziyi , Li Jingbin , Wang Rongjin , Bu Hongshi , Ruan Chengjiang 
Published: April 2024

ScopeCerebral ischemia‐reperfusion (IR) injury stands as a prominent global contributor to disability and mortality. Nervonic acid (NA), a bioactive elongated monounsaturated fatty acid, holds pivotal significance in human physiological well‐being. This research aims to explore the prophylactic effects and fundamental mechanisms of NA in a rat model of cerebral IR injury.Methods and resultsThrough the induction of middle cerebral artery occlusion, this study establishes a rat model of cerebral IR injury and comprehensively assesses the pharmacodynamic impacts of NA pretreatment. This evaluation involves behavioral analyses, histopathological examinations, and quantification of serum markers. Detailed mechanisms of nervonic acid's prophylactic effects are revealed through fecal metabolomics and 16S rRNA sequencing analyses. Our findings robustly support nervonic acid's capacity to ameliorate neurological impairments in rats afflicted with cerebral IR injury. Beyond its neurological benefits, NA demonstrates its potential by rectifying metabolic perturbations across diverse pathways, particularly those pertinent to unsaturated fatty acid metabolism. Additionally, NA emerges as a modulator of gut microbiota composition, notably by selectively enhancing vital genera likeLactobacillus.ConclusionThese comprehensive findings highlight the potential of incorporating NA as a functional component in dietary interventions aimed at targeting cerebral IR injury.
Maternal Brown Rice Diet during Pregnancy Promotes Adipose Tissue Browning in Offspring via Reprogramming PKA Signaling and DNA Methylation
Authors: Xue Lamei , Sun Juan , Sun Yujie , Wang Yu , Zhang Kuiliang , Fan Mingcong , Qian Haifeng , Li Yan , Wang Li 
Published: April 2024

ScopeBrown rice, the most consumed food worldwide, has been shown to possess beneficial effects on the prevention of metabolic diseases. However, the way in which maternal brown rice diet improves metabolism in offspring and the regulatory mechanisms remains unclear. The study explores the epigenetic regulation of offspring energy metabolic homeostasis by maternal brown rice diet during pregnancy.Methods and resultsFemale mice are fed brown rice during pregnancy, and then body phenotypes, the histopathological analysis, and adipose tissues biochemistry assay of offspring mice are detected. It is found that maternal brown rice diet significantly reduces body weight and fat mass, increases energy expenditure and heat production in offspring. Maternal brown rice diet increases uncoupling protein 1 (UCP1) protein level and upregulates the mRNA expression of thermogenic genes in adipose tissues. Mechanistically, protein kinase A (PKA) signaling is likely responsible in the induced thermogenic program in offspring adipocytes, and the progeny adipocytes browning program is altered due to decreased level of DNA methyltransferase 1 protein and hypomethylation of the transcriptional coregulator positive regulatory domain containing 16 (PRDM16).ConclusionsThese findings demonstrate that maternal brown rice during pregnancy improves offspring mice metabolic homeostasis via promoting adipose browning, and its mechanisms may be mediated by DNA methylation reprogramming.
Robustness of carbon‐ion radiotherapy against DNA damage repair associated radiosensitivity variation based on a biophysical model
Authors: Liew Hans , Tessonnier Thomas , Mein Stewart , Magro Giuseppe , Glimelius Lars , Coniavitis Elias , Held Thomas , Haberer Thomas , Abdollahi Amir , Debus Jürgen , Dokic Ivana , Mairani Andrea 
Published: April 2024

AbstractBackgroundInterpatient variation of tumor radiosensitivity is rarely considered during the treatment planning process despite its known significance for the therapeutic outcome.PurposeTo apply our mechanistic biophysical model to investigate the biological robustness of carbon ion radiotherapy (CIRT) against DNA damage repair interference (DDRi) associated patient‐to‐patient variability in radiosensitivity and its potential clinical advantages against conventional radiotherapy approaches.Methods and MaterialsThe “UNIfied and VERSatile bio response Engine” (UNIVERSE) was extended by carbon ions and its predictions were compared to a panel of in vitro and in vivo data including various endpoints and DDRi settings within clinically relevant dose and linear energy transfer (LET) ranges. The implications of UNIVERSE predictions were then assessed in a clinical patient scenario considering DDRi variance.ResultsUNIVERSE tests well against the applied benchmarks. While in vitro survival curves were predicted with an R2 > 0.92, deviations from in vivo RBE data were less than 5.6% The conducted paradigmatic patient plan study implies a markedly reduced significance of DDRi based radiosensitivity variability in CIRT (13% change of in target) compared to conventional radiotherapy (62%) and that boosting the LET within the target further amplifies this robustness of CIRT (8%). In the case of heightened tumor radiosensitivity, a dose de‐escalation strategy for photons allows a reduction of the maximum effective dose within the normal tissue (NT) from a of 2.65 to 1.64 Gy, which lies below the level found for CIRT ( = 2.41 Gy) for the analyzed plan and parameters. However, even after de‐escalation, the integral effective dose in the NT is found to be substantially higher for conventional radiotherapy in comparison to CIRT ( of 0.75, 0.46, and 0.24 Gy for the conventional plan, its de‐escalation and CIRT, respectively).ConclusionsThe framework offers adequate predictions of in vitro and in vivo radiation effects of CIRT while allowing the consideration of DRRi based solely on parameters derived from photon data. The results of the patient planning study underline the potential of CIRT to minimize important sources of interpatient divergence in therapy outcome, especially when combined with techniques that allow to maximize the LET within the tumor. Despite the potential of de‐escalation strategies for conventional radiotherapy to reduce the maximum effective dose in the NT, CIRT appears to remain a more favorable option due to its ability to reduce the integral effective dose within the NT.
Enhancing adaptive proton therapy through CBCT images: Synthetic head and neck CT generation based on 3D vision transformers
Authors: Viar‐Hernandez David , Molina‐Maza Juan Manuel , Vera‐Sánchez Juan Antonio , Perez‐Moreno Juan Maria , Mazal Alejandro , Rodriguez‐Vila Borja , Malpica Norberto , Torrado‐Carvajal Angel 
Published: April 2024

AbstractBackgroundProton therapy is a form of radiotherapy commonly used to treat various cancers. Due to its high conformality, minor variations in patient anatomy can lead to significant alterations in dose distribution, making adaptation crucial. While cone‐beam computed tomography (CBCT) is a well‐established technique for adaptive radiation therapy (ART), it cannot be directly used for adaptive proton therapy (APT) treatments because the stopping power ratio (SPR) cannot be estimated from CBCT images.PurposeTo address this limitation, Deep Learning methods have been suggested for converting pseudo‐CT (pCT) images from CBCT images. In spite of convolutional neural networks (CNNs) have shown consistent improvement in pCT literature, there is still a need for further enhancements to make them suitable for clinical applications.MethodsThe authors introduce the 3D vision transformer (ViT) block, studying its performance at various stages of the proposed architectures. Additionally, they conduct a retrospective analysis of a dataset that includes 259 image pairs from 59 patients who underwent treatment for head and neck cancer. The dataset is partitioned into 80% for training, 10% for validation, and 10% for testing purposes.ResultsThe SPR maps obtained from the pCT using the proposed method present an absolute relative error of less than 5% from those computed from the planning CT, thus improving the results of CBCT.ConclusionsWe introduce an enhanced ViT3D architecture for pCT image generation from CBCT images, reducing SPR error within clinical margins for APT workflows. The new method minimizes bias compared to CT‐based SPR estimation and dose calculation, signaling a promising direction for future research in this field. However, further research is needed to assess the robustness and generalizability across different medical imaging applications.
Deep learning‐based extraction of surface wave dispersion curves from seismic shot gathers
Authors: Chamorro Danilo , Zhao Jiahua , Birnie Claire , Staring Myrna , Fliedner Moritz , Ravasi Matteo 
Published: April 2024

AbstractMulti‐channel analysis of surface waves is a seismic method employed to obtain useful information about shear‐wave velocities in the near surface. A fundamental step in this methodology is the extraction of dispersion curves from dispersion spectra, with the latter usually obtained by applying specific processing algorithms onto the recorded shot gathers. Although the extraction process can be automated to some extent, it usually requires extensive quality control, which can be arduous for large datasets. We present a novel approach that leverages deep learning to identify a direct mapping between seismic shot gathers and their associated dispersion curves (both fundamental and first higher order modes), therefore by‐passing the need to compute dispersion spectra. Given a site of interest, a set of 1D compressional and shear velocities and density models are created using prior knowledge of the local geology; pairs of seismic shot gathers and Rayleigh‐wave phase dispersion curves are then numerically modelled and used to train a simplified residual network. The proposed approach is shown to achieve high‐quality predictions of dispersion curves on a synthetic test dataset and is, ultimately, successfully deployed on a field dataset. Various uncertainty quantification and convolutional neural network visualization techniques are also presented to assess the quality of the inference process and better understand the underlying learning process of the network. The predicted dispersion curves are inverted for both the synthetic and field data; in the latter case, the resulting shear‐wave velocity model is plausible and consistent with prior geological knowledge of the area. Finally, a comparison between the manually picked fundamental modes with the predictions from our model allows for a benchmark of the performance of the proposed workflow.
Validity of inpatient electronic health record‐based measures of oxygen‐related therapy in the United States: Lessons applicable for studying COVID‐19 endpoints
Authors: Fuller Candace C. , Rosen Edward , Rai Ashish , Cosgrove Austin , Miller Karla , Bright Patricia , Haffenreffer Katherine , Poland Russell E. , Pratt Natasha , Sands Kenneth , Shinde Mayura U. , Platt Richard , Cocoros Noelle M. , Klompas Michael , Dutcher Sarah K. 
Published: April 2024

AbstractIntroductionDuring the COVID‐19 pandemic, inpatient electronic health records (EHRs) have been used to conduct public health surveillance and assess treatments and outcomes. Invasive mechanical ventilation (MV) and supplemental oxygen (O2) use are markers of severe illness in hospitalized COVID‐19 patients. In a large US system (n = 142 hospitals), we assessed documentation of MV and O2 use during COVID‐19 hospitalization in administrative data versus nursing documentation.MethodsWe identified 319 553 adult hospitalizations with a COVID‐19 diagnosis, February 2020–October 2022, and extracted coded, administrative data for MV or O2. Separately, we developed classification rules for MV or O2 supplementation from semi‐structured nursing documentation. We assessed MV and O2 supplementation in administrative data versus nursing documentation and calculated ordinal endpoints of decreasing COVID‐19 disease severity. Nursing documentation was considered the gold standard in sensitivity and positive predictive value (PPV) analyses.ResultsIn nursing documentation, the prevalence of MV and O2 supplementation among COVID‐19 hospitalizations was 14% and 75%, respectively. The sensitivity of administrative data was 83% for MV and 41% for O2, with both PPVs above 91%. Concordance between sources was 97% for MV (κ = 0.85), and 54% for O2 (κ = 0.21). For ordinal endpoints, administrative data accurately identified intensive care and MV but underestimated hospitalizations with O2 requirements (42% vs. 18%).ConclusionsIn comparison to nursing documentation, administrative data under‐ascertained O2 supplementation but accurately estimated severe endpoints such as MV. Nursing documentation improved ascertainment of O2 among COVID‐19 hospitalizations and can capture oxygen requirements in adults hospitalized with COVID‐19 or other respiratory illnesses.
Prevalence of self‐medication practices among pregnant women in India: A systematic review and meta‐analysis
Authors: Chaudhary Vaibhav , Kumari Sweta , Khurana Navneet , Azharuddin Mohammad , Singh Aditya Pratap , Devi Varsha , Dhir Deepali , Pal Biplab 
Published: April 2024

AbstractBackgroundSelf‐medication practice among pregnant women is a global concern. However, its understanding in the Indian context is limited due to a lack of comprehensive studies.PurposeThis study aimed to comprehensively assess the prevalence of self‐medication, the medications used for self‐medication, diseases/conditions associated with self‐medication, and the reasons for self‐medication among Indian pregnant women.MethodsThis study was carried out following the guidelines outlined in the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA). A thorough search was done in PubMed, Embase, and Google Scholar to find articles that were published up until May 2023. Inclusion criteria comprised observational studies reporting self‐medication prevalence among pregnant women in India. Data were extracted using a standardized sheet, and a random‐effects model was applied to determine the overall prevalence of self‐medication using R software. The I2 statistic was employed to assess the heterogeneity among the studies.ResultsThis study analyzed eight studies with a collective sample size of 2208 pregnant women. The pooled prevalence of self‐medication among pregnant Indian women was 19.3% (95% CI: 7.5%–41.3%; I2 = 99%; p < 0.01). Common self‐treated conditions were cold, cough, fever, headache, and stomach disorders. Antipyretics, analgesics, antihistamines, and antacids were frequently used for self‐medication. The perception of mild ailment, immediate alleviation, convenience, time savings, and advice from family, friends, or the media were all reasons for self‐medication. Local pharmacies were the most usual source for obtaining drugs, and pharmacists, family, friends, and past prescriptions were common sources of medicine information.ConclusionsA low yet substantial number of pregnant women in India are engaged in self‐medication practices. Appropriate strategies need to be planned to reduce self‐medication practices to attain sustainable developmental goals for maternal health in India.
Design, characterization, and performance evaluation of novel PVA/CS/CNF/MOP TN ionic conductive hydrogels for flexible sensors
Authors: Xu Wenjun , Newton Md All Amin , Chen Zhuoming , Xin Binjie 
Published: April 2024

AbstractFlexible hydrogel polymers with excellent stretchability, high conductivity, and biocompatibility are widely used to prepare flexible sensing devices. However, it remains difficult to simultaneously combine excellent tensile capacity and high ion conducting ability into a simple hydrogel material. While PVA‐based hydrogels have been developed before, this work focuses on a unique preparation method. It solves the engineering problem of making hydrogels that have both high elongation at break and high ionic conductivity. In the study, we introduced cellulose nanofibers (CNFs) into polyvinyl alcohol (PVA)/chitosan (CS) cross‐linked polymer networks to form triple network (TN) composite hydrogel. The labyrinthine three‐dimensional (3D) honeycomb through‐hole network nanostructure generated by the addition of CNF provides the hydrogel with excellent mechanical properties. Then, the simple salting‐out strategy of muriate of potash (MOP) solution gives the hydrogel superior mechanical properties and high ionic conductivity. The prepared PVA/CS/CNF/MOP TN ionic conductive hydrogel not only exhibits excellent mechanical properties (toughness up to 2.39 MJ m−3, fracture strength up to 1.78 MPa, fracture strain up to 350%) but also has impressive ionic conductivity (highest to 8.4 S m−1). The flexible hydrogel strain sensor assembled from the PVA/CS/CNF/MOP TN ionic conductive hydrogel showed remarkable sensitivity (gauge factor for 6.9) and the ability to detect human motions reliably. Besides, the hydrogel also has maintained long‐term antimicrobial and antioxidant properties. The study provided a new perspective on designing multifunctional materials featuring strong mechanical properties and high ionic conductivity, contributing to the advancement of flexible sensor technologies.
Molecular crosstalk between insulin‐like growth factors and follicle‐stimulating hormone in the regulation of granulosa cell function
Authors: Hayes Emily , Winston Nicola , Stocco Carlos 
Published: April 2024

AbstractBackgroundThe last phase of folliculogenesis is driven by follicle‐stimulating hormone (FSH) and locally produced insulin‐like growth factors (IGFs), both essential for forming preovulatory follicles.MethodsThis review discusses the molecular crosstalk of the FSH and IGF signaling pathways in regulating follicular granulosa cells (GCs) during the antral‐to‐preovulatory phase.Main findingsIGFs were considered co‐gonadotropins since they amplify FSH actions in GCs. However, this view is not compatible with data showing that FSH requires IGFs to stimulate GCs, that FSH renders GCs sensitive to IGFs, and that FSH signaling interacts with factors downstream of AKT to stimulate GCs. New evidence suggests that FSH and IGF signaling pathways intersect at several levels to regulate gene expression and GC function.ConclusionFSH and locally produced IGFs form a positive feedback loop essential for preovulatory follicle formation in all species. Understanding the mechanisms by which FSH and IGFs interact to control GC function will help design new interventions to optimize follicle maturation, perfect treatment of ovulatory defects, improve in vitro fertilization, and develop new contraceptive approaches.
Assessing experimental silvicultural treatments enhancing structural complexity in a central European forest – BEAST time‐series analysis based on Sentinel‐1 and Sentinel‐2
Authors: Kacic Patrick , Gessner Ursula , Holzwarth Stefanie , Thonfeld Frank , Kuenzer Claudia 
Published: April 2024

AbstractAssessing the dynamics of forest structure complexity is a critical task in times of global warming, biodiversity loss and increasing disturbances in order to ensure the resilience of forests. Recent studies on forest biodiversity and forest structure emphasize the essential functions of deadwood accumulation and diversification of light conditions for the enhancement of structural complexity. The implementation of an experimental patch‐network in managed broad‐leaved forests within Germany enables the standardized analysis of various aggregated and distributed treatments characterized through diverse deadwood and light structures. To monitor the dynamics of enhanced forest structure complexity as seasonal and trend components, dense time‐series from high spatial resolution imagery of Sentinel‐1 (Synthetic‐Aperture Radar, SAR) and Sentinel‐2 (multispectral) are analyzed in time‐series decomposition models (BEAST, Bayesian Estimator of Abrupt change, Seasonal change and Trend). Based on several spatial statistics and a comprehensive catalog on spectral indices, metrics from Sentinel‐1 (n = 84) and Sentinel‐2 (n = 903) are calculated at patch‐level. Metrics best identifying the treatment implementation event are assessed by change point dates and probability scores. Heterogeneity metrics of Sentinel‐1 VH and Sentinel‐2 NMDI (Normalized Multi‐band Drought Index) capture the treatment implementation event most accurately, with clear advantages for the identification of aggregated treatments. In addition, aggregated structures of downed or no deadwood can be characterized, as well as more complex standing structures, such as snags or habitat trees. To conclude, dense time‐series of complementary high spatial resolution sensors have the potential to assess various aggregated forest structure complexities, thus supporting the continuous monitoring of forest habitats and functioning over time.
Functionalizing Yeast Lipid Droplets as Versatile Biomaterials
Authors: Suri Ankita , Hu Kevin K.Y. , Younas Tayyaba , Dumsday Geoff , Haritos Victoria S. 
Published: April 2024

AbstractLipid droplets (LD) are dynamic cellular organelles of ≈1 µm diameter in yeast where a neutral lipid core is surrounded by a phospholipid monolayer and attendant proteins. Beyond the storage of lipids, opportunities for LD engineering remain underdeveloped but they show excellent potential as new biomaterials. In this research, LD from yeast Saccharomyces cerevisiae is engineered to display mCherry fluorescent protein, Halotag ligand binding protein, plasma membrane binding v‐SNARE protein, and carbonic anhydrase enzyme via linkage to oleosin, an LD anchoring protein. Each protein‐oleosin fusion is coded via a single gene construct. The expressed fusion proteins are specifically displayed on LD and their functions can be assessed within cells by fluorescence confocal microscopy, TEM, and as isolated materials via AFM, flow cytometry, spectrophotometry, and by enzyme activity assay. LD isolated from the cell are shown to be robust and stabilize proteins anchored into them. These engineered LD function as reporters, bind specific ligands, guide LD and their attendant proteins into union with the plasma membrane, and catalyze reactions. Here, engineered LD functions are extended well beyond traditional lipid storage toward new material applications aided by a versatile oleosin platform anchored into LD and displaying linked proteins.