bluuebunny/crossref_metadata_2025_split huggingface dataset:
Design of a packed bed chemical looping (unmixed) combustion reactor for the application of heating liquid: A theoretical study
Authors: Faizal Amina , Deshpande Amol 
Published: July 2023

AbstractA packed bed reactor (PBR)‐based chemical looping combustion (CLC), also referred to as unmixed combustion (UMC), was reported as an alternative to fire in the literature. In this process, the oxygen carriers undergo oxidation and reduction reactions in alternate cycles using air and fuel as the reactive gases, respectively. The energy generated in these reactions can radially be transferred for applications like heating air which was successfully demonstrated. The results showed that 85–95% of the generated energy can radially be transferred while maintaining sustained combustion in the bed (at temperatures between 723 and 1173 K). While extending its application for heating liquids like water, it was found in the modeling and simulation study that the existing design resulted in quenching of the bed below 773 K in the oxidation cycle and achieving sustained combustion was not possible for all practical ranges of operating parameters. Hence it was decided to modify the existing system by increasing the volume ratio of the annular bed to the liquid section. Theoretical estimations revealed that increasing this ratio by four times or higher can result in maintaining sustained combustion conditions in the bed while having continuous radial heat transfer to the water flowing in the laminar range. The general guidelines for designing a UMC‐based liquid heating system were then prepared and used to propose a new design for water heating. The modeling and simulation studies for this proposed design also indicated that it is a feasible design. © 2023 Society of Chemical Industry and John Wiley & Sons, Ltd.
Microglial Nogo delays recovery following traumatic brain injury in mice
Authors: Glotfelty Elliot J. , Hsueh Shih‐Chang , Claybourne Quia , Bedolla Alicia , Kopp Katherine O. , Wallace Tonya , Zheng Binhai , Luo Yu , Karlsson Tobias E. , McDevitt Ross A. , Olson Lars , Greig Nigel H. 
Published: July 2023

AbstractNogo‐A, B, and C are well described members of the reticulon family of proteins, most well known for their negative regulatory effects on central nervous system (CNS) neurite outgrowth and repair following injury. Recent research indicates a relationship between Nogo‐proteins and inflammation. Microglia, the brain's immune cells and inflammation‐competent compartment, express Nogo protein, although specific roles of the Nogo in these cells is understudied. To examine inflammation‐related effects of Nogo, we generated a microglial‐specific inducible Nogo KO (MinoKO) mouse and challenged the mouse with a controlled cortical impact (CCI) traumatic brain injury (TBI). Histological analysis shows no difference in brain lesion sizes between MinoKO‐CCI and Control‐CCI mice, although MinoKO‐CCI mice do not exhibit the levels of ipsilateral lateral ventricle enlargement as injury matched controls. Microglial Nogo‐KO results in decreased lateral ventricle enlargement, microglial and astrocyte immunoreactivity, and increased microglial morphological complexity compared to injury matched controls, suggesting decreased tissue inflammation. Behaviorally, healthy MinoKO mice do not differ from control mice, but automated tracking of movement around the home cage and stereotypic behavior, such as grooming and eating (termed cage “activation”), following CCI is significantly elevated. Asymmetrical motor function, a deficit typical of unilaterally brain lesioned rodents, was not detected in CCI injured MinoKO mice, while the phenomenon was present in CCI injured controls 1‐week post‐injury. Overall, our studies show microglial Nogo as a negative regulator of recovery following brain injury. To date, this is the first evaluation of the roles microglial specific Nogo in a rodent injury model.
Association of time‐restricted feeding, arterial age, and arterial stiffness in adults with metabolic syndrome
Authors: Alinezhad‐Namaghi Maryam , Eslami Saeid , Nematy Mohsen , Rezvani Reza , Khoshnasab Adeleh , Bonakdaran Shokoofeh , Philippou Elena , Norouzy Abdolreza 
Published: July 2023

AbstractBackgroundTime‐restricted feeding (TRF) is a kind of intermittent fasting defined as eating and drinking only during a certain number of hours in a day. It has been suggested that intermittent fasting may improve cardiovascular risk factors. This study evaluated the association of TRF and arterial stiffness, using pulse wave velocity (PWV), pulse wave analysis, and arterial age in metabolic syndrome participants.MethodsA cohort study was carried out among metabolic syndrome adults who were followed over the Ramadan fasting period (used as a model of TRF since food was only allowed for about 8 h/day). The subjects were divided into Ramadan fasting and Ramadan nonfasting groups. The aortic PWV and central aortic pressure waveform were measured. Central systolic pressure, central pulse pressure, and indices of arterial compliance, such as augmentation pressure and augmentation index (AIx), were determined from waveform analysis.ResultsNinety‐five adults (31.57% female, age: 45.46 ± 9.10 years) with metabolic syndrome (based on the International Diabetes Federation definition) participated in this study. Ramadan fasting and Ramadan nonfasting groups were including 80 and 15 individuals respectively. A significant reduction was seen in PWV (0.29 m/s), central systolic pressure (4.03 mmHg), central pulse pressure (2.43 mmHg), central augmentation pressure (1.88 mmHg), and central AIx (2.47) in the Ramadan fasting group (p = 0.014, p < 0.001, p = 0.001, p = 0.003, and p = 0.036 respectively). There were no significant changes in these indices among the Ramadan nonfasting group.ConclusionsThis study suggested that TRF reduces arterial age and improves arterial stiffness among people with metabolic syndrome. This might be considered a beneficial nutrition strategy for extending healthspan (and perhaps longevity).
Ethical concerns surrounding sex prediction using noninvasive prenatal screening from pediatric endocrinologists' perspective
Authors: Kamoun Camilia , Rossi Wilma , Kilberg Marissa J. 
Published: July 2023

AbstractNoninvasive prenatal screening (NIPS) with predicted fetal sex chromosomes included in the results has become increasingly available for pregnant individuals. Predicted fetal sex chromosome results from NIPS are interpreted so as to equate sex chromosomes with sex and gender. As pediatric endocrinologists, we worry about how this use of NIPS harmfully reinforces sex and gender binaries and sets potentially inaccurate assumptions about what the identified chromosomes mean. We use a hypothetical case based on our clinical experience in which the NIPS report of fetal sex does not conform to expectations at birth to highlight ethical concerns surrounding this practice. The use of NIPS for fetal sex chromosome prediction has the potential to perpetuate stigma and bring psychological harm to parents and their future children, particularly those who are intersex, transgender, and gender diverse. The medical community should adopt an approach to the use of NIPS for fetal sex chromosome prediction that recognizes the spectrums of sex and gender to avoid reproducing stigma towards sex‐ and gender‐diverse individuals and associated harms.
State‐of‐the‐art 2023 on gene therapy for phenylketonuria
Authors: Martinez Michael , Harding Cary O. , Schwank Gerald , Thöny Beat 
Published: July 2023

AbstractPhenylketonuria (PKU) or hyperphenylalaninemia is considered a paradigm for an inherited (metabolic) liver defect and is, based on murine models that replicate all human pathology, an exemplar model for experimental studies on liver gene therapy. Variants in the PAH gene that lead to hyperphenylalaninemia are never fatal (although devastating if untreated), newborn screening has been available for two generations, and dietary treatment has been considered for a long time as therapeutic and satisfactory. However, significant shortcomings of contemporary dietary treatment of PKU remain. A long list of various gene therapeutic experimental approaches using the classical model for human PKU, the homozygous enu2/2 mouse, witnesses the value of this model to develop treatment for a genetic liver defect. The list of experiments for proof of principle includes recombinant viral (AdV, AAV, and LV) and non‐viral (naked DNA or LNP‐mRNA) vector delivery methods, combined with gene addition, genome, gene or base editing, and gene insertion or replacement. In addition, a list of current and planned clinical trials for PKU gene therapy is included. This review summarizes, compares, and evaluates the various approaches for the sake of scientific understanding and efficacy testing that may eventually pave the way for safe and efficient human application.
Magnetic Resonance Spectroscopy Spectral Registration Using Deep Learning
Authors: Ma David J. , Yang Yanting , Harguindeguy Natalia , Tian Ye , Small Scott A. , Liu Feng , Rothman Douglas L. , Guo Jia 
Published: July 2023

BackgroundDeep learning‐based methods have been successfully applied to MRI image registration. However, there is a lack of deep learning‐based registration methods for magnetic resonance spectroscopy (MRS) spectral registration (SR).PurposeTo investigate a convolutional neural network‐based SR (CNN‐SR) approach for simultaneous frequency‐and‐phase correction (FPC) of single‐voxel Meshcher–Garwood point‐resolved spectroscopy (MEGA‐PRESS) MRS data.Study TypeRetrospective.SubjectsForty thousand simulated MEGA‐PRESS datasets generated from FID Appliance (FID‐A) were used and split into the following: 32,000/4000/4000 for training/validation/testing. A 101 MEGA‐PRESS medial parietal lobe data retrieved from the Big GABA were used as the in vivo datasets.Field Strength/Sequence3T, MEGA‐PRESS.AssessmentEvaluation of frequency and phase offsets mean absolute errors were performed for the simulation dataset. Evaluation of the choline interval variance was performed for the in vivo dataset. The magnitudes of the offsets introduced were −20 to 20 Hz and −90° to 90° and were uniformly distributed for the simulation dataset at different signal‐to‐noise ratio (SNR) levels. For the in vivo dataset, different additional magnitudes of offsets were introduced: small offsets (0–5 Hz; 0–20°), medium offsets (5–10 Hz; 20–45°), and large offsets (10–20 Hz; 45–90°).Statistical TestsTwo‐tailed paired t‐tests for model performances in the simulation and in vivo datasets were used and a P‐value <0.05 was considered statistically significant.ResultsCNN‐SR model was capable of correcting frequency offsets (0.014 ± 0.010 Hz at SNR 20 and 0.058 ± 0.050 Hz at SNR 2.5 with line broadening) and phase offsets (0.104 ± 0.076° at SNR 20 and 0.416 ± 0.317° at SNR 2.5 with line broadening). Using in vivo datasets, CNN‐SR achieved the best performance without (0.000055 ± 0.000054) and with different magnitudes of additional frequency and phase offsets (i.e., 0.000062 ± 0.000068 at small, −0.000033 ± 0.000023 at medium, 0.000067 ± 0.000102 at large) applied.Data ConclusionThe proposed CNN‐SR method is an efficient and accurate approach for simultaneous FPC of single‐voxel MEGA‐PRESS MRS data.Evidence Level4Technical EfficacyStage 2
A validated LC–MS/MS method for quantitative determination of L‐dopa in Fagioli di Sarconi beans (Phaseolus vulgaris L.)
Authors: Tesoro Carmen , Lelario Filomena , Ciriello Rosanna , Bianco Giuliana , Acquavia Maria Assunta , Montoro Paola , Crescenzi Maria Assunta , Gregori Emanuela , Dell'Agli Mario , Piazza Stefano , Guerrieri Antonio , Di Capua Angela 
Published: July 2023

AbstractAn analytical method based on ultrasound assisted extraction (UAE) and liquid chromatography coupled to electrospray tandem mass spectrometry (LC–ESI/MS/MS) was validated and applied for determining L‐dopa in four ecotypes of Fagioli di Sarconi beans (Phaseolus vulgaris L.), marked with the European label PGI (Protected Geographical Indication). The selectivity of the proposed method was ensured by the specific fragmentation of the analyte. Simple isocratic chromatographic conditions and mass spectrometric detection in multiple reaction monitoring (MRM) acquisition mode were used for sensitive quantification. The LC–ESI/MS/MS method was validated within a linear range of 0.001–5.000 μg/mL. Values of 0.4 and 1.1 ng/mL were obtained for the limits of detection and quantification, respectively. The repeatability, inter‐day precision, and recovery values ranges were 0.6%–4.5%, 5.4%–9.9%, and 83%–93%, respectively. Fresh and dried beans, as well as pods, cultivated exclusively with organic methods avoiding any synthetic fertilizers and pesticides were analyzed showing an L‐dopa content ranging from 0.020 ± 0.005 to 2.34 ± 0.05 μg/g dry weight.
How nano‐iron chelate and arbuscular mycorrhizal fungi mitigate water stress in Lallemantia species: A growth and physio‐biochemical properties
Authors: Paravar Arezoo , Maleki Farahani Saeideh , Rezazadeh Alireza , Keshavarz Afshar Reza 
Published: July 2023

AbstractBackgroundWater deficit can seriously affect the growth, yield, and biochemical properties of plants; however, the application of nano‐iron chelate and arbuscular mycorrhizal fertilizers in water deficit has been considered one of the most promising methods to improve plant growth and enhance drought tolerance.AimsThis study aimed to investigate the effects of nano‐iron chelated (nFe) fertilizer and arbuscular mycorrhizal fungi (AMFs) on the growth, yield, root colonization, mycorrhizal dependency, water use efficiency (WUE), physio‐biochemical properties, and seed biochemical properties of Lallemantia iberica and Lallemantia royleana species under drought stress.MethodsA split‐factorial experiment was conducted using a randomized complete block design with three replications. The main plot consisted of three drought stress levels (according to the depletion of soil available water) of 30% (I1; without stress), 60% (I2; mild stress), and 90% (I3; severe stress). The subplots were the factorial combination of fertilizers (F) (without fertilizer, nano‐iron chelate [nFe], and AMFs) and plant species (S) of Lallemantia (L. iberica and L. royleana).ResultsA decrease in the irrigation regime caused a reduction in growth, seed yield, WUE, chlorophyll content, nutrient uptake, seed oil, and mucilage content, as well as an increase in lipid peroxidation and hydrogen peroxide. Compared with nFe, AMF significantly increased root colonization by mycorrhizal fungi in both species across irrigation regimes. Furthermore, AMF inoculation enhanced nutrient uptake, mucilage, oil, and fatty acids by increasing the WUE. Higher mycorrhizal dependency in inoculated plants played an important role in increasing proline and antioxidant activities in the leaves of the host plant. The water stress and fertilization treatments resulted in the highest root colonization, mycorrhizal dependency, proline content, and antioxidant enzyme activities in L. royleana, as well as the highest WUE, accumulation of fatty acids, and nutrient uptake was observed in L. iberica. Interestingly, the mycorrhization potential of Lallemantia sp. is clearly defined.ConclusionsThe results highlighted that AMFs can promote Lallemantia to increase seed mucilage and oil under water stress. Overall, we found that both Lallemantia species responded well to AMF inoculation under water deficit conditions, where nFe was found to be less effective.
Effects of aroma–taste interaction on the sensory attributes of rebaudiosides in soymilk and milk
Authors: Kim Yeo‐Hong , Hong Jae‐Hee 
Published: July 2023

AbstractBACKGROUNDRebaudioside A (Reb‐A) and rebaudioside M (Reb‐M) are intense natural sweeteners but can also elicit bitterness and a bitter aftertaste. In this study, the effect of vanilla and chocolate flavorings on the sensory attributes of Reb‐A and Reb‐M applied to soymilk and milk was investigated to identify whether the addition of flavoring could enhance the sweetness via aroma–taste interactions.RESULTSNine samples each of soymilk and milk were formulated by adding sucrose, Reb‐A and Reb‐M in three flavor conditions (no flavoring, vanilla, and chocolate). Descriptive analyses were conducted using nine panelists for the soymilk and eight panelists for the milk. Another descriptive analysis was conducted using the same samples with olfactory occlusion via the wearing of a nose clip to check whether the sweetness enhancement was due to olfactory input. The chocolate flavoring significantly enhanced the sweetness of Reb‐A and Reb‐M and reduced the bitterness, bitter aftertaste and astringency in both soymilk and milk. The vanilla flavoring was not as effective as the chocolate flavoring in enhancing sweetness. When the olfactory passage was closed with a nose clip, the sweetness enhancement and bitterness suppression were not detected in the samples.CONCLUSIONThe addition of chocolate flavoring could successfully improve the sensory profile of soymilk sweetened with Reb‐A through aroma–taste interactions. © 2023 Society of Chemical Industry.
Complementation‐reinforced network for integrated reconstruction and segmentation of pulmonary gas MRI with high acceleration
Authors: Li Zimeng , Xiao Sa , Wang Cheng , Li Haidong , Zhao Xiuchao , Zhou Qian , Rao Qiuchen , Fang Yuan , Xie Junshuai , Shi Lei , Ye Chaohui , Zhou Xin 
Published: July 2023

AbstractBackgroundHyperpolarized (HP) gas MRI enables the clear visualization of lung structure and function. Clinically relevant biomarkers, such as ventilated defect percentage (VDP) derived from this modality can quantify lung ventilation function. However, long imaging time leads to image quality degradation and causes discomfort to the patients. Although accelerating MRI by undersampling k‐space data is available, accurate reconstruction and segmentation of lung images are quite challenging at high acceleration factors.PurposeTo simultaneously improve the performance of reconstruction and segmentation of pulmonary gas MRI at high acceleration factors by effectively utilizing the complementary information in different tasks.MethodsA complementation‐reinforced network is proposed, which takes the undersampled images as input and outputs both the reconstructed images and the segmentation results of lung ventilation defects. The proposed network comprises a reconstruction branch and a segmentation branch. To effectively exploit the complementary information, several strategies are designed in the proposed network. Firstly, both branches adopt the encoder‐decoder architecture, and their encoders are designed to share convolutional weights for facilitating knowledge transfer. Secondly, a designed feature‐selecting block discriminately feeds shared features into decoders of both branches, which can adaptively pick suitable features for each task. Thirdly, the segmentation branch incorporates the lung mask obtained from the reconstructed images to enhance the accuracy of the segmentation results. Lastly, the proposed network is optimized by a tailored loss function that efficiently combines and balances these two tasks, in order to achieve mutual benefits.ResultsExperimental results on the pulmonary HP 129Xe MRI dataset (including 43 healthy subjects and 42 patients) show that the proposed network outperforms state‐of‐the‐art methods at high acceleration factors (4, 5, and 6). The peak signal‐to‐noise ratio (PSNR), structural similarity (SSIM), and Dice score of the proposed network are enhanced to 30.89, 0.875, and 0.892, respectively. Additionally, the VDP obtained from the proposed network has good correlations with that obtained from fully sampled images (r = 0.984). At the highest acceleration factor of 6, the proposed network promotes PSNR, SSIM, and Dice score by 7.79%, 5.39%, and 9.52%, respectively, in comparison to the single‐task models.ConclusionThe proposed method effectively enhances the reconstruction and segmentation performance at high acceleration factors up to 6. It facilitates fast and high‐quality lung imaging and segmentation, and provides valuable support in the clinical diagnosis of lung diseases.
A review of machine learning applications for the proton MR spectroscopy workflow
Authors: van de Sande Dennis M. J. , Merkofer Julian P. , Amirrajab Sina , Veta Mitko , van Sloun Ruud J. G. , Versluis Maarten J. , Jansen Jacobus F. A. , van den Brink Johan S. , Breeuwer Marcel 
Published: July 2023

AbstractThis literature review presents a comprehensive overview of machine learning (ML) applications in proton MR spectroscopy (MRS). As the use of ML techniques in MRS continues to grow, this review aims to provide the MRS community with a structured overview of the state‐of‐the‐art methods. Specifically, we examine and summarize studies published between 2017 and 2023 from major journals in the MR field. We categorize these studies based on a typical MRS workflow, including data acquisition, processing, analysis, and artificial data generation. Our review reveals that ML in MRS is still in its early stages, with a primary focus on processing and analysis techniques, and less attention given to data acquisition. We also found that many studies use similar model architectures, with little comparison to alternative architectures. Additionally, the generation of artificial data is a crucial topic, with no consistent method for its generation. Furthermore, many studies demonstrate that artificial data suffers from generalization issues when tested on in vivo data. We also conclude that risks related to ML models should be addressed, particularly for clinical applications. Therefore, output uncertainty measures and model biases are critical to investigate. Nonetheless, the rapid development of ML in MRS and the promising results from the reviewed studies justify further research in this field.
Efficient presolving methods for the influence maximization problem
Authors: Chen Sheng‐Jie , Chen Wei‐Kun , Dai Yu‐Hong , Yuan Jian‐Hua , Zhang Hou‐Shan 
Published: July 2023

AbstractWe consider the influence maximization problem (IMP) which asks for identifying a limited number of key individuals to spread influence in a network such that the expected number of influenced individuals is maximized. The stochastic maximal covering location problem (SMCLP) formulation is a mixed integer programming formulation that effectively approximates the IMP by the Monte‐Carlo sampling. For IMPs with a large‐scale network or a large number of samplings, however, the SMCLP formulation cannot be efficiently solved by existing exact algorithms due to its large problem size. In this paper, we attempt to develop presolving methods to reduce the problem size and hence enhance the capability of employing exact algorithms in solving large‐scale IMPs. In particular, we propose two effective presolving methods, called strongly connected nodes aggregation (SCNA) and isomorphic nodes aggregation (INA), respectively. The SCNA enables to build a new SMCLP formulation that is potentially much more compact than the existing one, and the INA further eliminates variables and constraints in the SMCLP formulation. A theoretical analysis on two special cases of the IMP is provided to demonstrate the strength of the SCNA and INA in reducing the problem size of the SMCLP formulation. We integrate the proposed presolving methods, SCNA and INA, into the Benders decomposition algorithm, which is recognized as one of the state‐of‐the‐art exact algorithms for solving the IMP. We show that the proposed SCNA and INA provide the possibility to develop a much faster separation algorithm for the Benders cuts. Numerical results demonstrate that with the SCNA and INA, the Benders decomposition algorithm is much more effective in solving the IMP in terms of solution time.