bluuebunny/crossref_metadata_2025_split huggingface dataset:
Microwave brain imaging system to detect brain tumor using metamaterial loaded stacked antenna array
Authors: Hossain Amran , Islam Mohammad Tariqul , Beng Gan Kok , Kashem Saad Bin Abul , Soliman Mohamed S. , Misran Norbahiah , Chowdhury Muhammad E. H. 
Published: October 2022

AbstractIn this paper, proposes a microwave brain imaging system to detect brain tumors using a metamaterial (MTM) loaded three-dimensional (3D) stacked wideband antenna array. The antenna is comprised of metamaterial-loaded with three substrate layers, including two air gaps. One 1 × 4 MTM array element is used in the top layer and middle layer, and one 3 × 2 MTM array element is used in the bottom layer. The MTM array elements in layers are utilized to enhance the performance concerning antenna’s efficiency, bandwidth, realized gain, radiation directionality in free space and near the head model. The antenna is fabricated on cost-effective Rogers RT5880 and RO4350B substrate, and the optimized dimension of the antenna is 50 × 40 × 8.66 mm3. The measured results show that the antenna has a fractional bandwidth of 79.20% (1.37–3.16 GHz), 93% radiation efficiency, 98% high fidelity factor, 6.67 dBi gain, and adequate field penetration in the head tissue with a maximum of 0.0018 W/kg specific absorption rate. In addition, a 3D realistic tissue-mimicking head phantom is fabricated and measured to verify the performance of the antenna. Later, a nine-antenna array-based microwave brain imaging (MBI) system is implemented and investigated by using phantom model. After that, the scattering parameters are collected, analyzed, and then processed by the Iteratively Corrected delay-multiply-and-sum algorithm to detect and reconstruct the brain tumor images. The imaging results demonstrated that the implemented MBI system can successfully detect the target benign and malignant tumors with their locations inside the brain.
Analysis of facial ultrasonography images based on deep learning
Authors: Lee Kang-Woo , Lee Hyung-Jin , Hu Hyewon , Kim Hee-Jin 
Published: October 2022

AbstractTransfer learning using a pre-trained model with the ImageNet database is frequently used when obtaining large datasets in the medical imaging field is challenging. We tried to estimate the value of deep learning for facial US images by assessing the classification performance for facial US images through transfer learning using current representative deep learning models and analyzing the classification criteria. For this clinical study, we recruited 86 individuals from whom we acquired ultrasound images of nine facial regions. To classify these facial regions, 15 deep learning models were trained using augmented or non-augmented datasets and their performance was evaluated. The F-measure scores average of all models was about 93% regardless of augmentation in the dataset, and the best performing model was the classic model VGGs. The models regarded the contours of skin and bones, rather than muscles and blood vessels, as distinct features for distinguishing regions in the facial US images. The results of this study can be used as reference data for future deep learning research on facial US images and content development.
Angular difference in human coronary artery governs endothelial cell structure and function
Authors: Katakia Yash T. , Kanduri Satyadevan , Bhattacharyya Ritobrata , Ramanathan Srinandini , Nigam Ishan , Kuncharam Bhanu Vardhan Reddy , Majumder Syamantak 
Published: October 2022

AbstractBlood vessel branch points exhibiting oscillatory/turbulent flow and lower wall shear stress (WSS) are the primary sites of atherosclerosis development. Vascular endothelial functions are essentially dependent on these tangible biomechanical forces including WSS. Herein, we explored the influence of blood vessel bifurcation angles on hemodynamic alterations and associated changes in endothelial function. We generated computer-aided design of a branched human coronary artery followed by 3D printing such designs with different bifurcation angles. Through computational fluid dynamics analysis, we observed that a larger branching angle generated more complex turbulent/oscillatory hemodynamics to impart minimum WSS at branching points. Through the detection of biochemical markers, we recorded significant alteration in eNOS, ICAM1, and monocyte attachment in EC grown in microchannel having 60o vessel branching angle which correlated with the lower WSS. The present study highlights the importance of blood vessel branching angle as one of the crucial determining factors in governing atherogenic-endothelial dysfunction.
Trehalose and tardigrade CAHS proteins work synergistically to promote desiccation tolerance
Authors: Nguyen Kenny , KC Shraddha , Gonzalez Tyler , Tapia Hugo , Boothby Thomas C. 
Published: October 2022

AbstractTardigrades are microscopic animals renowned for their ability to survive extreme desiccation. Unlike many desiccation-tolerant organisms that accumulate high levels of the disaccharide trehalose to protect themselves during drying, tardigrades accumulate little or undetectable levels. Using comparative metabolomics, we find that despite being enriched at low levels, trehalose is a key biomarker distinguishing hydration states of tardigrades. In vitro, naturally occurring stoichiometries of trehalose and CAHS proteins, intrinsically disordered proteins with known protective capabilities, were found to produce synergistic protective effects during desiccation. In vivo, this synergistic interaction is required for robust CAHS-mediated protection. This demonstrates that trehalose acts not only as a protectant, but also as a synergistic cosolute. Beyond desiccation tolerance, our study provides insights into how the solution environment tunes intrinsically disordered proteins’ functions, many of which are vital in biological contexts such as development and disease that are concomitant with large changes in intracellular chemistry.
Skyrmion motion and partitioning of domain wall velocity driven by repulsive interactions
Authors: Xing Xiangjun , Zhou Yan 
Published: October 2022

AbstractMagnetic skyrmions, as a whirling spin texture with axisymmetry, cannot be propelled directly by a uniform perpendicular magnetic field. Therefore, reported skyrmion motions have been induced using other sorts of stimuli — typically, electric currents in magnetic metals. Here, we propose to drive skyrmion motion, in a uniform perpendicular field, by intrinsic repulsive interactions among an outer domain wall (DW) and magnetic skyrmions. Through micromagnetic simulations, we demonstrate that the uniform perpendicular magnetic field can indeed displace magnetic skyrmions alongside the leading DW. At a fixed field strength, the velocity of the skyrmion train evolves according to a 1 / (Ns + 1) relation with Ns denoting the number of skyrmions. Based on the Thiele equation, we elucidate, analytically, the mechanism of the driven magnetic skyrmion motion as well as the velocity equipartition phenomenon and reveal that the skyrmion–DW and inter-skyrmion repulsive interactions offer the driving force for skyrmion motion. This study underlines the role of spin textures’ interaction in skyrmion dynamics, and opens an alternative route for skyrmion manipulation especially relevant to insulating magnets. Given the correspondence between ferromagnetism and ferroelectricity, we anticipate that the scheme should also work for polar skyrmions in ferroelectrics.
Data-driven analysis of a validated risk score for ovarian cancer identifies clinically distinct patterns during follow-up and treatment
Authors: Enroth Stefan , Ivansson Emma , Lindberg Julia Hedlund , Lycke Maria , Bergman Jessica , Reneland Anna , Stålberg Karin , Sundfeldt Karin , Gyllensten Ulf 
Published: October 2022

Abstract
Background
Ovarian cancer is the eighth most common cancer among women and due to late detection prognosis is poor with an overall 5-year survival of 30–50%. Novel biomarkers are needed to reduce diagnostic surgery and enable detection of early-stage cancer by population screening. We have previously developed a risk score based on an 11-biomarker plasma protein assay to distinguish benign tumors (cysts) from malignant ovarian cancer in women with adnexal ovarian mass.

Methods
Protein concentrations of 11 proteins were characterized in plasma from 1120 clinical samples with a custom version of the proximity extension assay. The performance of the assay was evaluated in terms of prediction accuracy based on receiver operating characteristics (ROC) and multiple hypothesis adjusted Fisher’s Exact tests on achieved sensitivity and specificity.

Results
The assay’s performance is validated in two independent clinical cohorts with a sensitivity of 0.83/0.91 and specificity of 0.88/0.92. We also show that the risk score follows the clinical development and is reduced upon treatment, and increased with relapse and cancer progression. Data-driven modeling of the risk score patterns during a 2-year follow-up after diagnosis identifies four separate risk score trajectories linked to clinical development and survival. A Cox proportional hazard regression analysis of 5-year survival shows that at time of diagnosis the risk score is the second-strongest predictive variable for survival after tumor stage, whereas MUCIN-16 (CA-125) alone is not significantly predictive.

Conclusion
The robust performance of the biomarker assay across clinical cohorts and the correlation with clinical development indicates its usefulness both in the diagnostic work-up of women with adnexal ovarian mass and for predicting their clinical course.
Fine‐grained classification of intracranial haemorrhage subtypes in head CT scans
Authors: Liu Pingping , Ning Gangjun , Shi Lida , Zhou Qiuzhan , Chen Xuan 
Published: October 2022

AbstractIntracranial haemorrhage (ICH) is a haemorrhagic disease that occurs in the ventricle or brain tissue and has a high probability of mortality and disability. For ICH, it is important to obtain a correct diagnosis in the early stages. Currently, ICH classification mainly depends on professional radiologists for manual diagnosis. Therefore, it is necessary to develop a method that can efficiently and rapidly diagnose ICH. In the field of ICH subtype classification, most studies directly use the existing convolutional neural network (CNN) to extract CT slice features. However, these existing networks have the following shortcomings: (1) insufficient discrimination of CT slice features leads to an inability to achieve satisfactory classification performance. (2) Most CT slice data sets of ICH have the serious problem of sample imbalance. (3) There is a correlation between subtypes; however, in previous studies, this correlation has been ignored. To solve these problems, the authors propose a classification algorithm for ICH subtypes applied to CT images. The CNN–RNN architecture was adopted to classify ICH subtypes. In the CNN module, the problem is viewed from a fine‐grained perspective, which solves the problem of insufficient feature discrimination in existing methods. A new loss function is also proposed to solve the problems of unbalanced data distribution and neglected dependencies among the labels. These parts are integrated into the proposed fine‐grained network architecture. The image embeddings were obtained by the CNN module and then input to the RNN module. The authors’ method was evaluated on the Radiological Society of North America 2019 Brain CT Haemorrhage (RSNA‐2019) benchmark. The experimental results demonstrated that the performance of the proposed method is state‐of‐the‐art.
Fast and accurate evaluation method of commutation failure in line‐commutated converter‐based high‐voltage direct current systems considering probability characteristics
Authors: Guo Yaxun , Tan Zhanpeng , Li Xiaohua , Liu Zhou , Wang Yulin , Cai Zexiang , Chen Zhe 
Published: October 2022

AbstractLine‐commutated converter‐based high‐voltage direct current (LCC‐HVDC) systems suffer from commutation failure (CF) when receiving‐end AC grid faults occur, which is a major drawback and threatens the security and stability of power systems. This paper presents a fast and accurate evaluation method of CF for the LCC‐HVDC systems, considering the CF probability characteristics. Firstly, based on the analysis of the CF probability characteristics from the view of short‐circuit impedance, the CF tolerance index (CFTI) and CF vulnerability index (CFVI) are proposed to quantitatively evaluate the LCC‐HVDC systems' risk of the possible‐ and inevitable‐occurrence CF, respectively. Secondly, four factors, that is, commutation voltage, fault occurrence time, DC current and firing angle command, are considered to calculate CF evaluation indices (CFEIs) and identify the CF risk area boundaries based on the theoretical analysis. Moreover, the relationships among short‐circuit impedance, fault position, and extinction angle are established based on the AC/DC decoupling method, which helps realize the decoupling of multi‐factors affecting CF during fault. Compared to earlier methods, this evaluation method combines the advantages of the accuracy of the electromagnetic transient simulation method and the rapidity of theoretical derivation. Finally, the validity and accuracy of the evaluation method are verified in the CIGRE HVDC benchmark model and IEEE 39‐bus system.
Variational and numerical analysis of aQ-tensor model for smectic-A liquid crystals
Authors: Xia Jingmin , Farrell Patrick E. 
Published: October 2022

We analyse an energy minimisation problem recently proposed for modelling smectic-A liquid crystals. The optimality conditions give a coupled nonlinear system of partial differential equations, with a second-order equation for the tensor-valued nematic order parameterQand a fourth-order equation for the scalar-valued smectic density variationu. Our two main results are a proof of the existence of solutions to the minimisation problem, and the derivation ofa priorierror estimates for its discretisation of the decoupled case (i.e.,q= 0) using theC0interior penalty method. More specifically, optimal rates in theH1andL2norms are obtained forQ, while optimal rates in a mesh-dependent norm andL2norm are obtained foru. Numerical experiments confirm the rates of convergence.
Imaging of Occupational and Environmental Lung Disease
Authors: Carroll Melissa B. , Kanne Jeffrey P. 
Published: October 2022

AbstractThe respiratory tract is continuously exposed to and filters toxins from the home and work environments. Certain occupations and environmental exposures can cause unique injuries to the upper and lower respiratory system. Despite increasing federal regulations in the workplace, occupation-associated lung disease is still a major cause of lung disease and disability and continues to evolve with changes in industry, regulation, and new emerging exposures and toxins. Establishing a diagnosis can be difficult, often due to long latency between exposure and clinical disease, insufficient patient history, and nonspecific or varying imaging appearance. Identifying key imaging features of occupational lung disease along with a multidisciplinary approach can aid in accurate and timely diagnosis. In this review, we will discuss the importance of a comprehensive patient history, multidisciplinary approach to diagnosis, and key imaging features of occupation-related lung injuries. Radiographic and computed tomographic findings will be described and illustrated.
Rapid identification of a major locus qPRL-C06 affecting primary root length in Brassica napus by QTL-seq
Authors: Wang Wei , Liu Haijiang , Xie Yiwen , King Graham John , White Philip John , Zou Jun , Xu Fangsen , Shi Lei 
Published: October 2022

Abstract

Background and Aims
Brassica napus is one of the most important oilseed crops worldwide. Seed yield of B. napus significantly correlates with the primary root length (PRL). The aims of this study were to identify quantitative trait loci (QTLs) for PRL in B. napus.

Methods
QTL-seq and conventional QTL mapping were jointly used to detect QTLs associated with PRL in a B. napus double haploid (DH) population derived from a cross between ‘Tapidor’ and ‘Ningyou 7’. The identified major locus was confirmed and resolved by an association panel of B. napus and an advanced backcross population. RNA-seq analysis of two long-PRL lines (Tapidor and TN20) and two short-PRL lines (Ningyou 7 and TN77) was performed to identify differentially expressed genes in the primary root underlying the target QTLs.

Key Results
A total of 20 QTLs impacting PRL in B. napus grown at a low phosphorus (P) supply were found by QTL-seq. Eight out of ten QTLs affecting PRL at a low P supply discovered by conventional QTL mapping could be detected by QTL-seq. The locus qPRL-C06 identified by QTL-seq was repeatedly detected at both an optimal P supply and a low P supply by conventional QTL mapping. This major constitutive QTL was further confirmed by regional association mapping. qPRL-C06 was delimited to a 0.77 Mb genomic region on chromosome C06 using an advanced backcross population. A total of 36 candidate genes within qPRL-C06 were identified that showed variations in coding sequences and/or exhibited significant differences in mRNA abundances in primary root between the long-PRL and short-PRL lines, including five genes involved in phytohormone biosynthesis and signaling.

Conclusions
These results both demonstrate the power of the QTL-seq in rapid QTL detection for root traits and will contribute to marker-assisted selective breeding of B. napus cultivars with increased PRL.
Large-scale lesion symptom mapping of depression identifies brain regions for risk and resilience
Authors: Trapp Nicholas T , Bruss Joel E , Manzel Kenneth , Grafman Jordan , Tranel Daniel , Boes Aaron D 
Published: October 2022

AbstractUnderstanding neural circuits that support mood is a central goal of affective neuroscience, and improved understanding of the anatomy could inform more targeted interventions in mood disorders. Lesion studies provide a method of inferring the anatomical sites causally related to specific functions, including mood. Here, we performed a large-scale study evaluating the location of acquired, focal brain lesions in relation to symptoms of depression. Five hundred and twenty-six individuals participated in the study across two sites (356 male, average age 52.4 ± 14.5 years). Each subject had a focal brain lesion identified on structural imaging and an assessment of depression using the Beck Depression Inventory-II, both obtained in the chronic period post-lesion (&gt;3 months). Multivariate lesion–symptom mapping was performed to identify lesion sites associated with higher or lower depression symptom burden, which we refer to as ‘risk’ versus ‘resilience’ regions. The brain networks and white matter tracts associated with peak regional findings were identified using functional and structural lesion network mapping, respectively. Lesion–symptom mapping identified brain regions significantly associated with both higher and lower depression severity (r = 0.11; P = 0.01). Peak ‘risk’ regions include the bilateral anterior insula, bilateral dorsolateral prefrontal cortex and left dorsomedial prefrontal cortex. Functional lesion network mapping demonstrated that these ‘risk’ regions localized to nodes of the salience network. Peak ‘resilience’ regions include the right orbitofrontal cortex, right medial prefrontal cortex and right inferolateral temporal cortex, nodes of the default mode network. Structural lesion network mapping implicated dorsal prefrontal white matter tracts as ‘risk’ tracts and ventral prefrontal white matter tracts as ‘resilience’ tracts, although the structural lesion network mapping findings did not survive correction for multiple comparisons. Taken together, these results demonstrate that lesions to specific nodes of the salience network and default mode network are associated with greater risk versus resiliency for depression symptoms in the setting of focal brain lesions.
The longitudinal loss of islet autoantibody responses from diagnosis of type 1 diabetes occurs progressively over follow-up and is determined by low autoantibody titres, early-onset, and genetic variants
Authors: Williams C L , Fareed R , Mortimer G L M , Aitken R J , Wilson I V , George G , Gillespie K M , Williams A J K ,   The BOX Study Group, Ballav Chitrabhanu , Dutta Atanu , Russell-Taylor Michelle , Besser Rachel , Bursell James , Chandran Shanthi , Patel Sejal , Smith Anne , Kenchaiah Manohara , Margabanthu Gomathi , Kavvoura Foteini , Yaliwal Chandan , Long A E 
Published: October 2022

Abstract
The clinical usefulness of post-diagnosis islet autoantibody levels is unclear and factors that drive autoantibody persistence are poorly defined in type 1 diabetes (T1D). Our aim was to characterise the longitudinal loss of islet autoantibody responses after diagnosis in a large, prospectively sampled UK cohort. Participants with T1D [n = 577] providing a diagnosis sample [range −1.0 to 2.0 years] and at least one post-diagnosis sample (&lt;32.0 years) were tested for autoantibodies to glutamate decarboxylase 65 (GADA), islet antigen-2 (IA-2A), and zinc transporter 8 (ZnT8A). Select HLA and non-HLA SNPs were considered. Non-genetic and genetic factors were assessed by multivariable logistic regression models for autoantibody positivity at initial sampling and autoantibody loss at final sampling. For GADA, IA-2A, and ZnT8A, 70.8%, 76.8%, and 40.1%, respectively, remained positive at the final sampling. Non-genetic predictors of autoantibody loss were low baseline autoantibody titres (P &lt; 0.0001), longer diabetes duration (P &lt; 0.0001), and age-at-onset under 8 years (P &lt; 0.01–-0.05). Adjusting for non-genetic covariates, GADA loss was associated with low-risk HLA class II genotypes (P = 0.005), and SNPs associated with autoimmunity RELA/11q13 (P = 0.017), LPP/3q28 (P = 0.004), and negatively with IFIH1/2q24 (P = 0.018). IA-2A loss was not associated with genetic factors independent of other covariates, while ZnT8A loss was associated with the presence of HLA A*24 (P = 0.019) and weakly negatively with RELA/11q13 (P = 0.049). The largest longitudinal study of islet autoantibody responses from diagnosis of T1D shows that autoantibody loss is heterogeneous and influenced by low titres at onset, longer duration, earlier age-at-onset, and genetic variants. These data may inform clinical trials where post-diagnosis participants are recruited.