bluuebunny/crossref_metadata_2025_split huggingface dataset:
A 32‐channel high‐impedance honeycomb‐shaped receive array for temporal lobes exploration at 11.7T
Authors: Gapais Paul‐François , Luong Michel , Giacomini Eric , Guillot Jules , Djaballah Elias , Gunamony Shajan , Chu Son , Hosseinnezhadian Sajad , Amadon Alexis 
Published: September 2024

AbstractPurposeThe newly operational 11.7T Iseult scanner provides an improved global SNR in the human brain. This gain in SNR can be pushed even further locally by designing region‐focused dense receive arrays. The temporal lobes are particularly interesting to neuroscientists as they are associated with language and concept recognition. Our main goal was to maximize the SNR in the temporal lobes and provide high‐acceleration capabilities for fMRI studies.MethodsWe designed and developed a 32‐channel receive array made of non‐overlapped hexagonal loops. The loops were arranged in a honeycomb pattern and targeted the temporal lobes. They were placed on a flexible neoprene cap closely fitting the head. A new stripline design with a high impedance was proposed and applied for the first time at 11.7T. Specific homebuilt miniaturized low‐impedance preamplifiers were directly mounted on the loops, providing preamplifier decoupling in a compact and modular design. Using an anatomical phantom, we experimentally compared the SNR and parallel imaging performance of the region‐focused cap to a 32‐channel whole‐brain receive array at 11.7T.ResultsThe experimental results showed a 1.7‐time higher SNR on average in the temporal lobes compared to the whole brain receive array. The g‐factor is also improved when undersampling in the antero‐posterior and head‐foot directions.ConclusionA significant SNR boost in the temporal lobes was demonstrated at 11.7T compared to the whole‐brain receive array. The parallel imaging capabilities were also improved in the temporal lobes in some acceleration directions.
Three contrasts in 3 min: Rapid, high‐resolution, and bone‐selective UTE MRI for craniofacial imaging with automated deep‐learning skull segmentation
Authors: Vu Brian‐Tinh Duc , Kamona Nada , Kim Yohan , Ng Jinggang J. , Jones Brandon C. , Wehrli Felix W. , Song Hee Kwon , Bartlett Scott P. , Lee Hyunyeol , Rajapakse Chamith S. 
Published: September 2024

AbstractPurposeUltrashort echo time (UTE) MRI can be a radiation‐free alternative to CT for craniofacial imaging of pediatric patients. However, unlike CT, bone‐specific MR imaging is limited by long scan times, relatively low spatial resolution, and a time‐consuming bone segmentation workflow.MethodsA rapid, high‐resolution UTE technique for brain and skull imaging in conjunction with an automatic segmentation pipeline was developed. A dual‐RF, dual‐echo UTE sequence was optimized for rapid scan time (3 min) and smaller voxel size (0.65 mm3). A weighted least‐squares conjugate gradient method for computing the bone‐selective image improves bone specificity while retaining bone sensitivity. Additionally, a deep‐learning U‐Net model was trained to automatically segment the skull from the bone‐selective images. Ten healthy adult volunteers (six male, age 31.5 ± 10 years) and three pediatric patients (two male, ages 12 to 15 years) were scanned at 3 T. Clinical CT for the three patients were obtained for validation. Similarities in 3D skull reconstructions relative to clinical standard CT were evaluated based on the Dice similarity coefficient and Hausdorff distance. Craniometric measurements were used to assess geometric accuracy of the 3D skull renderings.ResultsThe weighted least‐squares method produces images with enhanced bone specificity, suppression of soft tissue, and separation from air at the sinuses when validated against CT in pediatric patients. Dice similarity coefficient overlap was 0.86 ± 0.05, and the 95th percentile Hausdorff distance was 1.77 ± 0.49 mm between the full‐skull binary masks of the optimized UTE and CT in the testing dataset.ConclusionAn optimized MRI acquisition, reconstruction, and segmentation workflow for craniofacial imaging was developed.
A diffusion‐prepared reduced FOV sequence for prostate MRI near metallic implants
Authors: Lee Philip K. , Hess Jeremiah J. , Gomella Andrew A. , Loening Andreas M. , Hargreaves Brian A. 
Published: September 2024

AbstractPurposeTo enable diffusion weighted imaging in prostate patients with metallic total hip replacements in clinically feasible scan times for prostate cancer screening, and avoid distortion and dropout artifacts present in the conventionally used Echo Planar Imaging (EPI).MethodsA reduced field of view (FOV) diffusion‐prepared sequence that is robust to the B inhomogeneities produced by total hip replacements was achieved using high radiofrequency (RF) bandwidth pulses and manipulation for stimulated echo pathways. The reduced FOV along the A/P direction was obtained using slice‐select gradient reversal, and the prepared magnetization was imaged with a three‐dimensional RF‐spoiled gradient echo readout. The sequence was validated in phantom experiments, in vivo in healthy volunteers with and without total hip replacements, and in vivo in patients undergoing a standard MRI prostate exam.ResultsThe proposed sequence is robust to shading and distortion artifacts that are encountered by standard diffusion‐weighted EPI in the presence of moderate off‐resonance. Apparent diffusion coefficient estimates obtained by the proposed sequence were comparable to those obtained with diffusion‐weighted EPI.ConclusionAcquisition of distortionless diffusion weighted images of the prostate is feasible in patients with total hip replacements on conventional, whole‐body 3T MRI, using a b‐value of 800 and nominal resolution of 1.7 1.7 4 mm3 in scan times of 6 min.
T1 and T2 measurements across multiple 0.55T MRI systems using open‐source vendor‐neutral sequences
Authors: Keenan Kathryn E. , Tasdelen Bilal , Javed Ahsan , Ramasawmy Rajiv , Rizzo Rudy , Martin Michele N. , Stupic Karl F. , Seiberlich Nicole , Campbell‐Washburn Adrienne E. , Nayak Krishna S. 
Published: September 2024

AbstractPurposeTo compare T1 and T2 measurements across commercial and prototype 0.55T MRI systems in both phantom and healthy participants using the same vendor‐neutral pulse sequences, reconstruction, and analysis methods.MethodsStandard spin echo measurements and abbreviated protocol measurements of T1, B1, and T2 were made on two prototype 0.55 T systems and two commercial 0.55T systems using an ISMRM/NIST system phantom. Additionally, five healthy participants were imaged at each system using the abbreviated protocol for T1, B1, and T2 measurement. The phantom measurements were compared to NMR‐based reference measurements to determine accuracy, and both phantom and in vivo measurements were compared to assess reproducibility and differences between the prototype and commercial systems.ResultsVendor‐neutral sequences were implemented across all four systems, and the code for pulse sequences and reconstruction is freely available. For participants, there was no difference in the mean T1 and T2 relaxation times between the prototype and commercial systems. In the phantom, there were no significant differences between the prototype and commercial systems for T1 and T2 measurements using the abbreviated protocol.ConclusionQuantitative T1 and T2 measurements at 0.55T in phantom and healthy participants are not statistically different across the prototype and commercial systems.
Automated MRI‐based segmentation of intracranial arterial calcification by restricting feature complexity
Authors: Wang Xin , Canton Gador , Guo Yin , Zhang Kaiyu , Akcicek Halit , Yaman Akcicek Ebru , Hatsukami Thomas , Zhang Jin , Sun Beibei , Zhao Huilin , Zhou Yan , Shapiro Linda , Mossa‐Basha Mahmud , Yuan Chun , Balu Niranjan 
Published: September 2024

AbstractPurposeTo develop an automated deep learning model for MRI‐based segmentation and detection of intracranial arterial calcification.MethodsA novel deep learning model under the variational autoencoder framework was developed. A theoretically grounded dissimilarity loss was proposed to refine network features extracted from MRI and restrict their complexity, enabling the model to learn more generalizable MR features that enhance segmentation accuracy and robustness for detecting calcification on MRI.ResultsThe proposed method was compared with nine baseline methods on a dataset of 113 subjects and showed superior performance (for segmentation, Dice similarity coefficient: 0.620, area under precision‐recall curve [PR‐AUC]: 0.660, 95% Hausdorff Distance: 0.848 mm, Average Symmetric Surface Distance: 0.692 mm; for slice‐wise detection, F1 score: 0.823, recall: 0.764, precision: 0.892, PR‐AUC: 0.853). For clinical needs, statistical tests confirmed agreement between the true calcification volumes and predicted values using the proposed approach. Various MR sequences, namely T1, time‐of‐flight, and SNAP, were assessed as inputs to the model, and SNAP provided unique and essential information pertaining to calcification structures.ConclusionThe proposed deep learning model with a dissimilarity loss to reduce feature complexity effectively improves MRI‐based identification of intracranial arterial calcification. It could help establish a more comprehensive and powerful pipeline for vascular image analysis on MRI.
Superlinear Krylov convergence under streamline diffusion FEM for convection‐dominated elliptic operators
Authors: Karátson János 
Published: September 2024

AbstractThis paper studies the superlinear convergence of Krylov iterations under the streamline‐diffusion preconditioning operator for convection‐dominated elliptic problems. First, convergence results are given involving the diffusion parameter . Then the limiting case is studied on the operator level, and the convergence results are extended to this situation under some conditions, in spite of the lack of compactness of the perturbation operators. An explicit rate is also given.
The perceptions and experiences of caregivers of patients with dysphagia: A qualitative meta‐synthesis
Authors: Yi QiaoLi , Mao LiYe , Li WenYao , Shen Fan , Liao ZongFeng , Huang HaiShan , Li Ling 
Published: September 2024

AbstractAimsTo understand the perceptions and experiences of family caregivers of adult patients with dysphagia.BackgroundDysphagia is a common symptom and burdens caregivers greatly. There is a growing body of studies concentrating on caregivers and caregiving experiences. However, no qualitative meta‐synthesis has been conducted to explore the perceptions and experiences of family caregivers.DesignA qualitative meta‐ethnography.MethodsA search was conducted for relevant articles in six electronic databases (PubMed, Web of Science, CINAHL, Ovid, Cochrane Library, ProQuest) and two Chinese databases (CNKI, Wanfang Data) from inception to February 2023. The Joanna Briggs Institute Qualitative Assessment and Review Instrument (JBI‐QARI) was used to evaluate study quality. The meta‐ethnographic method was used to synthesize data from qualitative studies. The study was reported according to EQUATOR guidelines.ResultsEleven studies were included and three themes emerged: (1) emotion and perception, (2) change and challenge (3) adaption and coping.ConclusionThis review highlighted the challenges and positive coping experienced by caregivers. Findings directly inform the development and implementation of supportive interventions to reduce caregivers' stress and promote adaptive coping.Relevance to clinical practicePay attention to the needs of family caregivers of dysphagia. Family caregivers' perceived severity of dysphagia requires assessment. Caregivers need knowledge, support, and guidance to reduce their burden and fulfill their role.
A geographical paradox: microbiological profile and antibiotic resistance of diabetic foot infection in North West England
Authors: Balakrishnan Theenesh , Amarasena Danuksha K. , Bilal Ahmad , Nath Upamanyu , Ariff Amir , Pillai Anand 
Published: September 2024

AbstractBackground: The microbiological composition of diabetic foot infection (DFI) and its antimicrobial resistance exhibit variations in different parts of the world.Aims: This study aimed to shed light on the microbial load associated with DFI and the patterns of antibiotic resistance in Northwest England.Methods: This was a retrospective descriptive study that included 67 patients (55 male [78.6%]). The mean age at diagnosis was 45.6 years (standard deviation, 15.8). The culture of deep tissue samples was analysed together with antibiotic resistance.Results: A total of 114 causative pathogens were identified. Of note, 40 patients (60.00%) had polymicrobial infections. Moreover, 58.77% of the microbial cohort was composed of gram‐positive bacteria. Staphylococcus spp. were found in 32 patients (47.76%) and were the most prevalent pathogen in our cohort. Anaerobic bacteria were found in 17 patients (25.37%) and were the second most common pathogen in our cohort. Corynebacterium spp., Streptococcus spp. and Enterococcus spp. were identified in 11 (16.42%), 10 (14.93%) and 9 (13.43%) patients, respectively. Among the gram‐negative bacteria, Escherichia spp. were found in 7 patients (10.45%), Enterobacter spp. were found in 6 patients (8.96%), Klebsiella spp. were found in 4 patients (5.97%), Proteus spp. were found in 4 patients (5.97%) and Alcaligenes spp. were found in 2 patients (2.99%). The remaining less common organisms collectively accounted for 1.49% prevalence. Regarding antibiotic therapy, the highest resistance was observed for ciprofloxacin (12 [17.91%]), followed by amoxicillin (11 [16.42%]), penicillin (10 [14.93%]), clarithromycin (7 [10.45%]), trimethoprim (7 [10.45%]), doxycycline (6 [8.96%]) and piperacillin/tazobactam (5 [7.46%]).Conclusions: In contrast to the predominant aerobic gram‐negative bacteria in Asia, the Middle East and Africa, our study found a paradoxically higher prevalence of gram‐positive and anaerobic bacteria in North West England. Moreover, our study found a high incidence of resistance to ciprofloxacin and amoxicillin.
The importance of image quality in AI visual inspection systems
Authors: Dolberg Sagi 
Published: September 2024

AbstractThe use of AI for visual inspection is currently making great strides in sectors like automotive, metals and machinery manufacturing. However, those investing in the technology not only expect a system that will be superior to manual inspection, but an error rate close to zero in defect detection. Let us look at the importance of image quality and why the variability in the production environment is the single greatest challenge that AI engineers must face.
Increasing productivity by combining laser processes
Authors: Hofele Markus , Buser Matthias 
Published: September 2024

AbstractIncreasing the material ablation rate in micromachining of precise geometries by ultrashort‐pulsed laser treatment with high average power causes a decrease of achievable surface quality due to heat accumulation. To overcome this drawback, combined laser processes offer a significant increase of the overall productivity by the implementation of laser cleaning and polishing processes that require negligible additional processing time, but allow material removal to be performed with maximum process efficiency.
AdheScan – adhesive failure surface inspection system
Authors: Kuhlmann Luca , Lamott André , Thomsen Armin , Mühlenfeld‐Keßler Heiko , Knigge Anja 
Published: September 2024

AbstractAdhesive bonding is an established joining technique with many applications in for example aerospace, lightweight construction, and the automotive industry. It places particularly high demands on materials, processes, and quality assurance, requiring extensive development and qualification procedures. Many process samples generated for testing are still evaluated manually by experts. The AdheScan provides a quantifiable, simplified and more accurate evaluation of adhesive failure surfaces.
Understanding the mechanisms of food effect on omaveloxolone pharmacokinetics through physiologically based biopharmaceutics modeling
Authors: Pepin Xavier J. H. , Hynes Scott M. , Zahir Hamim , Walker Deborah , Semmens Lois Q. , Suarez‐Sharp Sandra 
Published: September 2024

AbstractOmaveloxolone is a nuclear factor (erythroid‐derived 2)‐like 2 activator approved in the United States and the European Union for the treatment of patients with Friedreich ataxia aged ≥16 years, with a recommended dosage of 150 mg orally once daily on an empty stomach. The effect of the US Food and Drug Administration (FDA) high‐fat breakfast on the pharmacokinetic profile of omaveloxolone observed in study 408‐C‐1703 (NCT03664453) deviated from the usual linear correlation between fed/fasted maximum plasma concentration (Cmax) and area under the concentration–time curve (AUC) ratios reported for various oral drugs across 323 food effect studies. Here, physiologically based biopharmaceutics modeling (PBBM) was implemented to predict and explain the effect of the FDA high‐fat breakfast on a 150‐mg dose of omaveloxolone. The model was developed and validated based on dissolution and pharmacokinetic data available across dose‐ranging, food effect, and drug–drug interaction clinical studies. PBBM predictions support clinical observations of the unique effect of a high‐fat meal on omaveloxolone pharmacokinetic profile, in which the Cmax increased by 350% with only a 15% increase in the AUC. Key parameters influencing omaveloxolone pharmacokinetics in the fasted state based on a parameter sensitivity analysis included bile salt solubilization, CYP3A4 activity, drug substance particle size distribution, and permeability. Mechanistically, in vivo omaveloxolone absorption was solubility and dissolution rate limited. However, in the fed state, higher bile salt solubilization led to more rapid dissolution with predominant absorption in the upper gastrointestinal tract, resulting in increased susceptibility to first‐pass gut extraction; this accounts for the lack of correlation between Cmax and AUC for omaveloxolone.
Accounting for black carbon refractive index in atmospheric radiation
Authors: Li Jiangnan , Digby Ruth , von Salzen Knut 
Published: September 2024

AbstractBecause of the fractal aggregated structure of black carbon (BC), black carbon refractive index measurements are difficult. There are substantial differences among the over 40 existing measurement schemes and no two schemes are the same. Three typical BC refractive index schemes are chosen to explore the difference in black carbon optical properties and the consequences of the radiative effect. Two schemes are widely used in climate models, and the third is from a newer measurement in 2016. It is shown that black carbon optical properties are sensitive to different refractive indices. The relative differences in extinction coefficient and single scattering albedo can be over 100%. In addition, by using Maxwell–Garnett and Bruggeman mixing rules, it has been found that the effect of internal mixing on aerosol optical properties depends strongly on the choice of refractive index. Using a one‐dimensional radiative transfer model under clear‐sky conditions, we demonstrate that the choice of black carbon refractive index influences the inferred radiative effect. Using the more recent (2016) scheme for pure black carbon can increase the top‐of‐atmosphere radiative effect by 20% relative to the currently widely used lowest‐absorbing scheme. For internally mixed aerosol, the sign of the radiative effect can change depending on which refractive index is used.
The biostimulatory effect of humic‐based soil amendment on plant growth, root nodulation, symbiotic nitrogen fixation and yield of field pea (Pisum sativum L.)
Authors: Rathor Pramod , Upadhyay Punita , Ullah Aman , Warkentin Thomas D. , Gorim Linda Yuya , Thilakarathna Malinda S. 
Published: September 2024

AbstractIntroductionPea is one of the most important pulse legumes globally due to its high protein, which is due to its ability to fix atmospheric nitrogen through a symbiotic relationship with nitrogen‐fixing rhizobia. Symbiotic nitrogen fixation (SNF) should be optimized to maximize nitrogen fixation and achieve higher yields with more grain protein. The use of humic‐based soil amendments in crop production has garnered considerable attention in recent years due to their biostimulatory effect in improving plant growth, yield, nutritional quality and soil health. This study examines the impact of a humic‐based soil amendment (Humalite) on root nodulation, SNF, plant growth, yield and grain protein of pea.Materials and MethodsChemical characterization was performed using Fourier transform infrared spectroscopy (FTIR). Pea plants inoculated with Rhizobia were grown in pots under greenhouse conditions with five different Humalite rates (0, 200, 400, 800 and 1600 kg ha−1). SNF capacity was assessed using the 15N‐isotope dilution method.ResultsFTIR analysis revealed the abundant presence of hydroxyl (‐OH) and carboxyl (‐CO) functional groups in Humalite. Plants treated with Humalite displayed augmented root traits [root length (21%–50%), root surface area (24%–51%), volume (26%–53%), average nodule weight (11%–91%)], plant biomass [shoots (13%–29%) and roots (29%–54%)], shoot nitrogen concentration (12%–33%), shoot total nitrogen content (38%–53%), percentage nitrogen derived from the atmosphere (8%–14%) and total shoot nitrogen fixed (48%–80%) compared to the control plants at the flowering stage. Furthermore, at seed maturity stage, plants treated with Humalite at 400 and 1600 kg ha−1 exhibited a significant increase in plant biomass (4%–14%), number of seeds (8%–16%), seed weight (3%–11%), seed nitrogen content (8%–20%) and total seed nitrogen fixed (7%–22%) compared to the control plants.ConclusionsThese findings demonstrate that humic‐based soil amendment can effectively enhance plant growth, root nodulation, SNF and seed yield, thereby supporting sustainable agricultural practices.
Multi‐Biomarker Profiling for Precision Diagnosis of Lung Cancer
Authors: Seo Dongkwon , Choi Byeong Hyeon , La Ju A , Kim Youngjae , Kang Taewook , Kim Hyun Koo , Choi Yeonho 
Published: September 2024

AbstractMulti‐biomarker analysis can enhance the accuracy of the single‐biomarker analysis by reducing the errors caused by genetic and environmental differences. For this reason, multi‐biomarker analysis shows higher accuracy in early and precision diagnosis. However, conventional analysis methods have limitations for multi‐biomarker analysis because of their long pre‐processing times, inconsistent results, and large sample requirements. To solve these, a fast and accurate precision diagnostic method is introduced for lung cancer by multi‐biomarker profiling using a single drop of blood. For this, surface‐enhanced Raman spectroscopic immunoassay (SERSIA) is employed for the accurate, quick, and reliable quantification of biomarkers. Then, it is checked the statistical relation of the multi‐biomarkers to differentiate between healthy controls and lung cancer patients. This approach has proven effective; with 20 µL of blood serum, lung cancer is diagnosed with 92% accuracy. It also accurately identifies the type and stage of cancer with 87% and 85%, respectively. These results show the importance of multi‐biomarker analysis in overcoming the challenges posed by single‐biomarker diagnostics. Furthermore, it markedly improves multi‐biomarker‐based analysis methods, illustrating its important impact on clinical diagnostics.