bluuebunny/crossref_metadata_2025_split huggingface dataset:
A compact multiband band slot antenna for wireless applications
Authors: Khan Imran , Ali Tanweer , Devanagavi Geeta D. , Sudhindra Kumbhashi R. , Biradar Rajashekhar C. 
Published: February 2019

A compact (20 × 20 × 1.6 mm3) planar four band antenna operating at 1.35 (lower Global Navigation Satellite System [GNSS]), 2.57 (lower Worldwide Microwave Access [WiMAX]), 4.57 (C‐band) and for 5.13 GHz (Wireless Local Area Network [WLAN]) is presented. By utilizing slot method in the radiating patch and the ground plane the aforementioned operating bands are achieved. The antenna has a miniaturized dimension of 0.09λ0 × 0.09λ0 × 0.007λ0 (where λ0 is the free space wavelength at the lower frequency of 1.35 GHz, and exhibits good impedance match, acceptable radiation efficiency and stable radiation pattern at the targeted frequencies.
Cover Image, Volume 94, Issue 3
Authors: Hu Xingyou , Shen Gaotian , Hu Tao , Guan Guoping , Wang Lu 
Published: February 2019

The cover image is based on the Research Article Use of an in vitro dynamic culture system to assess flow shear forces upon cell adhesion within different structures by Xingyou Hu et al., DOI: 10.1002/jctb.5834.
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Retention on antiretroviral therapy during Universal Test and Treat implementation in Zomba district, Malawi: a retrospective cohort study
Authors: Alhaj Mohammad , Amberbir Alemayehu , Singogo Emmanuel , Banda Victor , van Lettow Monique , Matengeni Alfred , Kawalazira Gift , Theu Joe , Jagriti Megh R , Chan Adrienne K , van Oosterhout Joep J 
Published: February 2019

AbstractIntroductionSince June 2016, the national HIV programme in Malawi has adopted Universal Test and Treat (UTT) guidelines requiring that all persons who test HIV positive will be referred to start antiretroviral therapy (ART). Although there is strong evidence from clinical trials that early initiation of ART leads to reduced morbidity and mortality, the impact of UTT on retention on ART in real‐life programmatic settings in Africa is not yet known.MethodsWe conducted a retrospective cohort study in Zomba district, Malawi to compare ART outcomes of patients who initiated ART under 2016 UTT guidelines and those who started ART prior to rollout of UTT (pre‐UTT). We analysed data from 32 rural and urban health facilities of various sizes. Cox proportional hazards modelling was used to determine the independent risk factors of attrition from ART at 12 months. All analyses were adjusted for clustering by health facility using a robust standard errors approach.ResultsAmong 1492 patients (mean age 34.4 years, 933 (63%) female) who initiated ART during the study period, 501 were enrolled in the pre‐UTT cohort and 911 during UTT. At 12 months, retention on ART in the UTT cohort was higher than in the pre‐UTT cohort 83.0% (95% confidence interval (CI): 81.0% to 85.0%) versus 76.2% (95% CI 73.9% to 78.5%). Adolescents, aged 10 to 19 years (adjusted hazard ratio (aHR) 1.53; 95% CI 1.01 to 2.32), and women who were pregnant or breastfeeding at ART initiation (aHR 1.87; 95% CI 1.30 to 2.38) were at higher risk of attrition in the combined pre‐UTT and UTT cohort.ConclusionsRetention on ART was nearly 6% higher after UTT introduction. Young adults and women who were pregnant or breastfeeding at the start of ART were at increased risk of attrition, emphasizing the need for targeted interventions for these groups to achieve the 90‐90‐90 UNAIDS targets in the UTT era.
Synaptic energy metabolism and neuronal excitability, in sickness and health
Authors: Oyarzabal Alfonso , Marin‐Valencia Isaac 
Published: February 2019

AbstractMost of the energy produced in the brain is dedicated to supporting synaptic transmission. Glucose is the main fuel, providing energy and carbon skeletons to the cells that execute and support synaptic function: neurons and astrocytes, respectively. It is unclear, however, how glucose is provided to and used by these cells under different levels of synaptic activity. It is even more unclear how diseases that impair glucose uptake and oxidation in the brain alter metabolism in neurons and astrocytes, disrupt synaptic activity, and cause neurological dysfunction, of which seizures are one of the most common clinical manifestations. Poor mechanistic understanding of diseases involving synaptic energy metabolism has prevented the expansion of therapeutic options, which, in most cases, are limited to symptomatic treatments. To shed light on the intersections between metabolism, synaptic transmission, and neuronal excitability, we briefly review current knowledge of compartmentalized metabolism in neurons and astrocytes, the biochemical pathways that fuel synaptic transmission at resting and active states, and the mechanisms by which disorders of brain glucose metabolism disrupt neuronal excitability and synaptic function and cause neurological disease in the form of epilepsy.
Highly‐accelerated volumetric brain examination using optimized wave‐CAIPI encoding
Authors: Polak Daniel , Cauley Stephen , Huang Susie Y. , Longo Maria Gabriela , Conklin John , Bilgic Berkin , Ohringer Ned , Raithel Esther , Bachert Peter , Wald Lawrence L. , Setsompop Kawin 
Published: February 2019

BackgroundRapid volumetric imaging protocols could better utilize limited scanner resources.PurposeTo develop and validate an optimized 6‐minute high‐resolution volumetric brain MRI examination using Wave‐CAIPI encoding.Study TypeProspective.Population/SubjectsTen healthy subjects and 20 patients with a variety of intracranial pathologies.Field Strength/SequenceAt 3 T, MPRAGE, T2‐weighted SPACE, SPACE FLAIR, and SWI were acquired at 9‐fold acceleration using Wave‐CAIPI and for comparison at 2–4‐fold acceleration using conventional GRAPPA.AssessmentExtensive simulations were performed to optimize the Wave‐CAIPI protocol and minimize both g‐factor noise amplification and potential T1/T2 blurring artifacts. Moreover, refinements in the autocalibrated reconstruction of Wave‐CAIPI were developed to ensure high‐quality reconstructions in the presence of gradient imperfections. In a randomized and blinded fashion, three neuroradiologists assessed the diagnostic quality of the optimized 6‐minute Wave‐CAIPI exam and compared it to the roughly 3× slower GRAPPA accelerated protocol using both an individual and head‐to‐head analysis.Statistical TestA noninferiority test was used to test whether the diagnostic quality of Wave‐CAIPI was noninferior to the GRAPPA acquisition, with a 15% noninferiority margin.ResultsAmong all sequences, Wave‐CAIPI achieved negligible g‐factor noise amplification (gavg ≤ 1.04) and burring artifacts from T1/T2 relaxation. Improvements of our autocalibration approach for gradient imperfections enabled increased robustness to gradient mixing imperfections in tilted‐field of view (FOV) prescriptions as well as variations in gradient and analog‐to‐digital converter (ADC) sampling rates. In the clinical evaluation, Wave‐CAIPI achieved similar mean scores when compared with GRAPPA (MPRAGE: ØW = 4.03, ØG = 3.97; T2w SPACE: ØW = 4.00, ØG = 4.00; SPACE FLAIR: ØW = 3.97, ØG = 3.97; SWI: ØW = 3.93, ØG = 3.83) and was statistically noninferior (N = 30, P < 0.05 for all sequences).Data ConclusionThe proposed volumetric brain exam retained comparable image quality when compared with the much longer conventional protocol.Level of Evidence: 2Technical Efficacy: Stage 1J. Magn. Reson. Imaging 2019;50:961–974.
Management of intussusception in patients with melanoma
Authors: Perez Matthew C. , Sun James , Farley Clara , Han Dale , Sun Alexander H. , Narayan Deepak , Lowe Michael , Delman Keith A. , Messina Jane L. , Gonzalez Ricardo J. , Sondak Vernon K. , Khushalani Nikhil I. , Zager Jonathan S. 
Published: February 2019

AbstractBackgroundIncreased cross‐sectional imaging for surveillance of metastatic melanoma has led to more diagnoses of asymptomatic intussusception.MethodsWe performed a multi‐institutional retrospective review of patient records with a history of metastatic melanoma and a diagnosis of intussusception. Patients were divided into three groups: 1) asymptomatic patients without current evidence of melanoma (no evidence of disease [NED]); 2) asymptomatic intussusception and known active metastatic melanoma; 3) symptomatic intussusception and known active metastatic melanoma; the number of patients requiring surgery and intraoperative findings were recorded.ResultsWe reviewed 73 patients diagnosed with intussusception from 2004 to 2017. Among asymptomatic patients with NED (n = 16), 14 spontaneously resolved and 2 underwent pre‐emptive surgery without abnormal intraoperative findings. Of asymptomatic patients with active metastatic disease (n = 32), 25 were initially observed and 7 underwent pre‐emptive surgery and 9 of the 25 initially observed patients required surgery for development of symptoms. In this group, all 16 patients undergoing surgery (50% of the group) had intraoperative findings of intussusception and/or metastatic intestinal melanoma.. All symptomatic patients with metastatic melanoma (n = 25) underwent surgery; all had intraoperative findings of intussusception and/or metastatic melanoma except 1 (Meckel's diverticulum).ConclusionAsymptomatic patients with NED do not require surgery and intussusception will likely resolve spontaneously. Asymptomatic patients with known metastatic melanoma may be initially observed, but a low threshold for surgery should be maintained. Symptomatic patients with known metastases should undergo surgery.
Synthesis and antiwear properties of dicarboxylic acid derivatives containing dialkyl dithiophosphate groups
Authors: Sim Dea Seon , Kim Young‐Wun , Joeng Noh‐Hee , Kang Ho‐Cheol 
Published: February 2019

AbstractThe use of lubricants has grown more widespread with industrial development. Lubricants usually contain additives with heavy metals such as zinc and molybdenum, increasing the emission of heavy metal contamination of soil and ocean. This study synthesised antiwear additives without heavy metal components in a simple and efficient manner by using alkane diacid derivatives produced from biomass and dithiophosphate instead of petroleum‐based compounds. A four‐ball wear test was performed to examine tribological performance in relation to changes in the alkyl chain length of the diacid and dithiophosphate of the synthesised antiwear additive, and the results were compared with the performance of zinc dialkyl‐dithiophosphorus (ZDDP), an antiwear additive that contains heavy metals. The tribological performance of the antiwear additives produced from synthesised dialkyl carboxylic acid derivatives was analysed based on the measurements of wear scar diameter. The wear scar diameter was 0.48 mm for the synthesised antiwear additive, whereas the ZDDP at a concentration of 0.05% gave a diameter of 0.82 mm. In the case of the synthesised antiwear additive, tribological performance did not improve for concentrations higher than 0.05%. An analysis of the carbon chain length of the dialkyl dithiophospate and dicarboxylic acid showed that the wear scar diameter was 0.72 mm when the alkyl chain length of R1 was 4. This diameter dropped below 0.48 mm when the alkyl chain length was greater than 8. When the number of carbons of dicarboxylic acid moiety was increased from 4 to 11, the antiwear performance improved slightly, with the wear scar diameter decreasing from 0.52 to 0.48 mm.
Simultaneous Deconvolution of the Bivariate Molecular Weight and Chemical Composition Distribution of Ethylene/1‐Hexene Copolymers
Authors: Hornchaiya Chat , Anantawaraskul Siripon , Soares João B. P. , Mehdiabadi Saeid 
Published: February 2019

AbstractThe bivariate molecular weight and chemical composition distribution (MWD×CCD) of ethylene/1‐hexene copolymers can be measured using TREF×GPC cross fractionation characterization (CFC). In this work, the experimental MWD×CCD of ethylene/1‐hexene copolymers made with a Ziegler–Natta catalyst under different polymerization conditions are measured by CFC and deconvoluted to identify the minimum number of site types present in the catalyst. Blends of ethylene/1‐hexene copolymers produced with a metallocene catalyst with known MWD×CCD are used to validate the proposed technique. This is a powerful methodology to better understand the nature of active sites on multisite catalysts, and can be beneficial for the development of copolymers with precisely controlled microstructures.
Simultaneously Improving the Thermal, Flame‐Retardant and Mechanical Properties of Epoxy Resins Modified by a Novel Multi‐Element Synergistic Flame Retardant
Authors: Li Zhinong , Chen Mingfeng , Li Shanshan , Fan Xianmou , Liu Canpei 
Published: February 2019

AbstractTo obtain epoxy resins with satisfactory thermal, flame retardant, and mechanical properties, a novel multi‐element synergistic flame retardant (PPVSZ) is synthesized through the reaction between PH of 9,10‐dihydro‐9‐oxa‐10‐phosphaphenanthrene‐10‐oxide (DOPO) and CC of polysilazane (PVSZ) and utilized as a multi‐element synergistic flame retardant for epoxy resins. The flame retardant mechanism is explored by XPS and SEM, confirming that the excellent flame‐retardance efficiency owes itself to an optimal flame retardant way which jointly exerts the flame‐retardant effects in the gaseous and condensed phase. The thermal properties deduced from DSC, TGA, and DMA, indicate the glass transition temperature, maximum weight loss rate, and char yields at 700 °C for EP‐2 increase by about 5.0 °C, 8.4 °C and 8.8%, respectively. Furthermore, mechanical properties such as impact strength, tensile strength, and flexural strength are also increased by 45.38%, 14.16%, and 17.43%, respectively, which show that the incorporation of PPVSZ does not deteriorate the mechanical properties of modified resin. All the results demonstrate that epoxy resins modified by PPVSZ not only have good effect on the flame retardance, but also have good improvement on thermal and mechanical properties, indicating the potential for applications in many fields requiring fire safety.
Machine learning for automated quality assurance in radiotherapy: A proof of principle using EPID data description
Authors: El Naqa Issam , Irrer Jim , Ritter Tim A. , DeMarco John , Al‐Hallaq Hania , Booth Jeremy , Kim Grace , Alkhatib Ahmad , Popple Richard , Perez Mario , Farrey Karl , Moran Jean M. 
Published: February 2019

PurposeDeveloping automated methods to identify task‐driven quality assurance (QA) procedures is key toward increasing safety, efficacy, and efficiency. We investigate the use of machine learning (ML) methods for possible visualization, automation, and targeting of QA, and assess its performance using multi‐institutional data.MethodsTo enable automated analysis of QA data given its higher dimensional nature, we used nonlinear kernel mapping with support vector data description (SVDD) driven approaches. Instead of using labeled data as in typical support vector machine (SVM) applications, which requires exhaustive annotation, we applied a clustering extension of SVDD, which identifies the minimal enclosing hypersphere in the feature space defined by a kernel function separating normal operations from possible failures (i.e., outliers). In our case, QA test data are mapped by a Gaussian kernel to a higher dimensional feature space and then the minimal enclosing sphere was identified. This sphere, when mapped back to the input data space along the principal components, can separate the data into several components, each enclosing a separate cluster of QA points that could be used to evaluate tolerance boundaries and test reliability. We evaluated this approach for gantry sag, radiation field shift, and [multileaf collimator (MLC)] offset data acquired using electronic portal imaging devices (EPID), as representative examples.ResultsData from eight LINACS and seven institutions (n = 119) were collected. A standardized EPID image of a phantom with fiducials provided deviation estimates between the radiation field and phantom center at four cardinal gantry angles. Deviation measurements in the horizontal direction (0°, 180°) were used to determine the gantry sag and deviations in the vertical direction (90°, 270°) were used to determine the field shift. These measurements were fed into the SVDD clustering algorithm with varying hypersphere radii (Gaussian widths). For gantry sag analysis, two clusters were identified one of which contained 2.5% of the outliers and also exceeded the 1 mm tolerance set by TG‐142. In the case of field shifts, SVM clustering identified two distinct classes of measurements primarily driven by variations in the second principal component at 270°. Results from MLC analysis identified one outlier cluster (0.34%) along Leaf offset Constancy (LoC) axis that coincided with TG‐142 limits.ConclusionMachine learning methods based on SVDD clustering are promising for developing automated QA tools and providing insights into their reliability and reproducibility.
Convolutional neural network‐based approach for segmentation of left ventricle myocardial scar from 3D late gadolinium enhancement MR images
Authors: Zabihollahy Fatemeh , White James A. , Ukwatta Eranga 
Published: February 2019

PurposeAccurate three‐dimensional (3D) segmentation of myocardial replacement fibrosis (i.e., scar) is emerging as a potentially valuable tool for risk stratification and procedural planning in patients with ischemic cardiomyopathy. The main purpose of this study was to develop a semiautomated method using a 3D convolutional neural network (CNN)‐based for the segmentation of left ventricle (LV) myocardial scar from 3D late gadolinium enhancement magnetic resonance (LGE‐MR) images.MethodsOur proposed CNN is built upon several convolutional and pooling layers aimed at choosing appropriate features from LGE‐MR images to distinguish between myocardial scar and healthy tissues of the left ventricle. In contrast to previous methods that consider image intensity as the sole feature, CNN‐based algorithms have the potential to improve the accuracy of scar segmentation through the creation of unconventional features that separate scar from normal myocardium in the feature space. The first step of our pipeline was to manually delineate the left ventricular myocardium, which was used as the region of interest for scar segmentation. Our developed algorithm was trained using 265,220 volume patches extracted from ten 3D LGE‐MR images, then was validated on 450,454 patches from a testing dataset of 24 3D LGE‐MR images, all obtained from patients with chronic myocardial infarction. We evaluated our method in the context of several alternative methods by comparing algorithm‐generated segmentations to manual delineations performed by experts.ResultsOur CNN‐based method reported an average Dice similarity coefficient (DSC) and Jaccard Index (JI) of 93.63% ± 2.6% and 88.13% ± 4.70%. In comparison to several previous methods, including K‐nearest neighbor (KNN), hierarchical max flow (HMF), full width at half maximum (FWHM), and signal threshold to reference mean (STRM), the developed algorithm reported significantly higher accuracy for DSC with a P‐value less than 0.0001.ConclusionsOur experimental results demonstrated that our CNN‐based proposed method yielded the highest accuracy of all contemporary LV myocardial scar segmentation methodologies, inclusive of the most widely used signal intensity‐based methods, such as FWHM and STRM. To our knowledge, this is the first description of LV myocardial scar tissue segmentation from 3D LGE‐MR images using a CNN‐based method.
U‐Net based deep learning bladder segmentation in CT urography
Authors: Ma Xiangyuan , Hadjiiski Lubomir M. , Wei Jun , Chan Heang‐Ping , Cha Kenny H. , Cohan Richard H. , Caoili Elaine M. , Samala Ravi , Zhou Chuan , Lu Yao 
Published: February 2019

ObjectivesTo develop a U‐Net–based deep learning approach (U‐DL) for bladder segmentation in computed tomography urography (CTU) as a part of a computer‐assisted bladder cancer detection and treatment response assessment pipeline.Materials and methodsA dataset of 173 cases including 81 cases in the training/validation set (42 masses, 21 with wall thickening, 18 normal bladders), and 92 cases in the test set (43 masses, 36 with wall thickening, 13 normal bladders) were used with Institutional Review Board approval. An experienced radiologist provided three‐dimensional (3D) hand outlines for all cases as the reference standard. We previously developed a bladder segmentation method that used a deep learning convolution neural network and level sets (DCNN‐LS) within a user‐input bounding box. However, some cases with poor image quality or with advanced bladder cancer spreading into the neighboring organs caused inaccurate segmentation. We have newly developed an automated U‐DL method to estimate a likelihood map of the bladder in CTU. The U‐DL did not require a user‐input box and the level sets for postprocessing. To identify the best model for this task, we compared the following models: (a) two‐dimensional (2D) U‐DL and 3D U‐DL using 2D CT slices and 3D CT volumes, respectively, as input, (b) U‐DLs using CT images of different resolutions as input, and (c) U‐DLs with and without automated cropping of the bladder as an image preprocessing step. The segmentation accuracy relative to the reference standard was quantified by six measures: average volume intersection ratio (AVI), average percent volume error (AVE), average absolute volume error (AAVE), average minimum distance (AMD), average Hausdorff distance (AHD), and the average Jaccard index (AJI). As a baseline, the results from our previous DCNN‐LS method were used.ResultsIn the test set, the best 2D U‐DL model achieved AVI, AVE, AAVE, AMD, AHD, and AJI values of 93.4 ± 9.5%, −4.2 ± 14.2%, 9.2 ± 11.5%, 2.7 ± 2.5 mm, 9.7 ± 7.6 mm, 85.0 ± 11.3%, respectively, while the corresponding measures by the best 3D U‐DL were 90.6 ± 11.9%, −2.3 ± 21.7%, 11.5 ± 18.5%, 3.1 ± 3.2 mm, 11.4 ± 10.0 mm, and 82.6 ± 14.2%, respectively. For comparison, the corresponding values obtained with the baseline method were 81.9 ± 12.1%, 10.2 ± 16.2%, 14.0 ± 13.0%, 3.6 ± 2.0 mm, 12.8 ± 6.1 mm, and 76.2 ± 11.8%, respectively, for the same test set. The improvement for all measures between the best U‐DL and the DCNN‐LS were statistically significant (P < 0.001).ConclusionCompared to a previous DCNN‐LS method, which depended on a user‐input bounding box, the U‐DL provided more accurate bladder segmentation and was more automated than the previous approach.
ENU‐induced mutant allele of Dnah1, ferf1, causes abnormal sperm behavior and fertilization failure in mice
Authors: Hu Jianjun , Lessard Carl , Longstaff Charles , O'Brien Marilyn , Palmer Kristina , Reinholdt Laura , Eppig John , Schimenti John , Handel Mary Ann 
Published: February 2019

AbstractGiven attention to both contraception and treatment of infertility, there is a need to identify genes and sequence variants required for mammalian fertility. Recent unbiased mutagenesis strategies have expanded horizons of genetic control of reproduction. Here we show that male mice homozygous for the ethyl‐nitroso‐urea‐induced ferf1 (fertilization failure 1) mutation are infertile, producing apparently normal sperm that does not fertilize oocytes in standard fertilization in vitro fertilization assays. The ferf1 mutation is a single‐base change in the Dnah1 gene, encoding an axoneme‐associated dynein heavy chain, and previously associated with male infertility in both mice and humans. This missense mutation causes a single‐amino‐acid change in the DNAH1 protein in ferf1 mutant mice that leads to abnormal sperm clumping, aberrant sperm motility, and the inability of sperm to penetrate the oocyte's zona pellucida; however, the ferf1 mutant sperm is competent to fertilize zona‐free oocytes. Taken together, the various mutations affecting the DNAH1 protein in both mouse and human produce a diversity of phenotypes with both subtle and considerable differences. Thus, future identification of the interacting partners of DNAH1 might lead to understanding its unique function among the sperm dyneins.