bluuebunny/crossref_metadata_2025_split huggingface dataset:
Observations on early fungal infections with relevance for replant disease in fine roots of the rose rootstock Rosa corymbifera 'Laxa'
Authors: Grunewaldt-Stöcker G. , Popp C. , Baumann A. , Fricke S. , Menssen M. , Winkelmann T. , Maiss E. 
Published: December 2020

AbstractReplant disease is a worldwide phenomenon affecting various woody plant genera and species, especially within the Rosaceae. Compared to decades of intensive studies regarding replant disease of apple (ARD), the replant disease of roses (RRD) has hardly been investigated. The etiology of RRD is also still unclear and a remedy desperately needed. In greenhouse pot trials with seedlings of the RRD-sensitive rootstockRosa corymbifera‘Laxa’ cultured in replant disease affected soils from two different locations, early RRD symptom development was studied in fine roots. In microscopic analyses we found similarities to ARD symptoms with regards to structural damages, impairment in the root hair status, and necroses and blackening in the cortex tissue. Examinations of both whole mounts and thin sections of fine root segments revealed frequent conspicuous fungal infections in association with the cellular disorders. Particularly striking were fungal intracellular structures with pathogenic characteristics that are described for the first time. Isolated fungi from these tissue areas were identified by means of ITS primers, and many of them were members of the Nectriaceae. In a next step, 35 of these isolates were subjected to a multi-locus sequence analysis and the results revealed that several genera and species were involved in the development of RRD within a single rose plant. Inoculations with selected single isolates (Rugonectria rugulosaandIlyonectria robusta) in a Perlite assay confirmed their pathogenic relationship to early necrotic host plant reactions, and symptoms were similar to those exhibited in ARD.
Cell fate clusters in ICM organoids arise from cell fate heredity and division: a modelling approach
Authors: Liebisch Tim , Drusko Armin , Mathew Biena , Stelzer Ernst H. K. , Fischer Sabine C. , Matthäus Franziska 
Published: December 2020

AbstractDuring the mammalian preimplantation phase, cells undergo two subsequent cell fate decisions. During the first decision, the trophectoderm and the inner cell mass are formed. Subsequently, the inner cell mass segregates into the epiblast and the primitive endoderm. Inner cell mass organoids represent an experimental model system, mimicking the second cell fate decision. It has been shown that cells of the same fate tend to cluster stronger than expected for random cell fate decisions. Three major processes are hypothesised to contribute to the cell fate arrangements: (1) chemical signalling; (2) cell sorting; and (3) cell proliferation. In order to quantify the influence of cell proliferation on the observed cell lineage type clustering, we developed an agent-based model accounting for mechanical cell–cell interaction, i.e. adhesion and repulsion, cell division, stochastic cell fate decision and cell fate heredity. The model supports the hypothesis that initial cell fate acquisition is a stochastically driven process, taking place in the early development of inner cell mass organoids. Further, we show that the observed neighbourhood structures can emerge solely due to cell fate heredity during cell division.
Online identification of aerodynamics with fast time‐varying features using Kalman filter
Authors: Zhang Wanxin , Zhu Jihong 
Published: December 2020

AbstractThis paper investigates the online identification method of the unsteady aerodynamics in the post‐stall manoeuvres. The main unsteady features are hysteresis and fast time varying due to the complex flow structure at large angles of attack. When limited test data are available, the data‐driven model cannot be identified accurately, especially in dealing with the fast time‐varying features, so online identification is required. A constant acceleration model‐based Kalman filter is constructed by taking into account the unsteady item in the aerodynamic model. Unlike the previous work which only uses the information of second‐order moments of errors, the proposed method uses the maximum correntropy criterion. Simulation studies according to the wind tunnel test data show that the proposed method can effectively track the fast time‐varying features and estimate the aerodynamic coefficients with high accuracy.
Degradation assessment of in‐service aerial bundled cables in coastal areas leading to prognosis using infrared thermography
Authors: Hassan Moez ul , Khan Tariq Mairaj , Abbas Sumayya , Shahid Muhammad Atayyab , Tariq Syed Talha , Amir Faisal 
Published: December 2020

AbstractOf late, cross‐linked polyethylene insulated aerial bundled cables have replaced conventional copper conductors being resistant to electricity pilferage, offer lower maintenance costs and reduced power losses. However, in coastal areas, aerial bundled cables experience frequent insulation breakdown causing sudden cable failures leading to unexpected power shutdowns. Condition monitoring, employing sophisticated diagnostic techniques, has thus become a major requirement of the energy sector. The reported research work utilizes infrared thermography as one such candidate NDT technique. A novel framework is proposed to investigate the progressive shift in the statistical parameters of the temperature distribution in aerial bundled cables’ insulation, as it degrades over time. The cumulative distribution function of pixel intensity data from a healthy/ operational aerial bundled cable is first compared with a reported failed aerial bundled cable. A prominent difference in 0.9 cumulative distribution function values is observed in the temperature distributions. Therefore, 0.9 cumulative distribution function values are used for degradation quantification with respect to ageing. Degradation rates are then computed using periodically acquired thermographic data from operational aerial bundled cables, installed at two different locations in a coastal region, each subjected to different marine climate. The proposed technique proved effective in degradation rate assessment of insulation of operational aerial bundled cables. Consequently, the results achieved can also be utilized for remaining useful life prediction of these cables.
Multi‐style Chinese art painting generation of flowers
Authors: Fu Feifei , Lv Jiancheng , Tang Chenwei , Li Mao 
Published: December 2020

AbstractWith the proposal and development of Generative Adversarial Networks, the great achievements in the field of image generation are made. Meanwhile, many works related to the generation of painting art have also been derived. However, due to the difficulty of data collection and the fundamental challenge from freehand expressions, the generation of traditional Chinese painting is still far from being perfect. This paper specialises in Chinese art painting generation of flowers, which is important and classic, by deep learning method. First, an unpaired flowers paintings data set containing three classic Chinese painting style: line drawing, meticulous, and ink is constructed. Then, based on the collected dataset, a Flower‐Generative Adversarial Network framework to generate multi‐style Chinese art painting of flowers is proposed. The Flower‐Generative Adversarial Network, consisting of attention‐guided generators and discriminators, transfers the style among line drawing, meticulous, and ink by an adversarial training way. Moreover, in order to solve the problem of artefact and blur in image generation by existing methods, a new loss function called Multi‐Scale Structural Similarity to force the structure preservation is introduced. Extensive experiments show that the proposed Flower‐Generative Adversarial Network framework can produce better and multi‐style Chinese art painting of flowers than existing methods.
Noise robust intuitionistic fuzzy c‐means clustering algorithm incorporating local information
Authors: Yang Zhenzhen , Xu Pengfei , Yang Yongpeng , Kang Bin 
Published: December 2020

AbstractThe human brain magnetic resonance image (MRI) is always contaminated by noise and has uncertainty on the boundary between different tissues. These characteristics bring challenges to the human brain image segmentation. To handle these limitations, many variants of standard fuzzy c‐means (FCM) algorithm have been proposed. Some methods attempt to incorporate the local spatial information in the standard FCM algorithm. However, they can't solve the problem of data uncertainty very well. And some other methods can handle the problem of data uncertainty, but they are sensitive to noise since it doesn't incorporate any local spatial information. In this paper, we propose a noise robust intuitionistic fuzzy c‐means (NR‐IFCM) algorithm, which can handle noise and uncertainty problems simultaneously. In order to process the human brain MRI with noise better, we introduce a noise robust intuitionistic fuzzy set (NR‐IFS) which is noise robust in this NR‐IFCM algorithm. Meanwhile, in order to handle the data uncertainty, we also introduce a new intuitionistic fuzzy factor to this NR‐IFCM algorithm which combine the local gray‐level and the spatial information together. A large number of experimental results on human brain MRI validate the effectiveness and the superiority of our proposed NR‐IFCM algorithm.
Identification of plant disease images via a squeeze‐and‐excitation MobileNet model and twice transfer learning
Authors: Chen Junde , Zhang Defu , Suzauddola Md , Nanehkaran Yaser Ahangari , Sun Yuandong 
Published: December 2020

AbstractCrop diseases have a devastating effect on agricultural production, and serious diseases can lead to harvest failure entirely. Recent developments in deep learning have greatly improved the accuracy of image identification. In this study, we investigated the transfer learning of deep convolutional neural networks and modified the network structure to improve the learning capability of plant lesion characteristics. The MobileNet with squeeze‐and‐excitation (SE) block was selected in our approach. Integrating the merits of both, the pre‐trained MobileNet and SE block were fused to form a new network, which we termed the SE‐MobileNet, and was used to identify the plant diseases. In particular, the transfer learning was performed twice to obtain the optimum model. The first phase trained the model for the extended layers while the bottom convolution layers were frozen with the pre‐trained weights on ImageNet; by loading the model trained in the first phase, the second phase retrained the model using the target dataset. The proposed procedure provides a significant increase in efficiency relative to other state‐of‐the‐art methods. It reaches an average accuracy of 99.78% in the public dataset with clear backdrops. Even under multiple classes and heterogeneous background conditions, the average accuracy realises 99.33% for identifying the rice disease types. The experimental findings show the feasibility and effectiveness of the proposed procedure.
High‐speed target tracking algorithm for the pulse‐sequence‐based image sensor
Authors: Xu Jiangtao , Wang Xiangfeng , Gao Zhiyuan , Nie Kaiming 
Published: December 2020

AbstractThis paper proposed a high‐speed target tracking algorithm based on the pulse‐sequence‐based image sensor. The algorithm separates the target from the background through the statistical distribution of the pulses in a single frame of data. In order to achieve more accurate target tracking, the phenomenon of image lag in the process of capturing the high‐speed moving target is studied. The positioning deviation is compensated by estimating the image lag length of the moving target. The algorithm is implemented with pipeline processing on field‐programmable gate array (FPGA). The positioning accuracy of the algorithm is verified with input data from a pulse‐sequence image sensor model. The verification results show that the maximum position deviation of the target at different speeds is 0.94 pixels. In the experiment of tracking a 40 m/s moving ball, this algorithm can provide real‐time position information within 44 ns.
Vehicle license plate recognition for fog‐haze environments
Authors: Jin Xianli , Tang Ruocong , Liu Linfeng , Wu Jiagao 
Published: December 2020

AbstractThe technique of vehicle license plate recognition can recognize and count the vehicles automatically, and thus many applications regarding the vehicles are greatly facilitated. However, the recognitions of vehicle license plates are extremely difficult especially in some fog‐haze environments because the fog and haze blur the boundaries and characters of license plates significantly, which makes the license plates hard to be detected or recognised. To this end, this paper proposes a vehicle License Plate Recognition method for Fog‐Haze environments (LPRFH). In LPRFH, a dark channel prior algorithm based on the local estimation of atmospheric light value is applied to dehaze the blurred images preliminarily. Then, the images are further dehazed, and the license plate regions are detected through a Joint Further‐dehazing and Region‐extracting Model on basis of an object detection convolution neural network. Finally, the image super‐resolution is accomplished with a convolution‐enhanced super‐resolution convolutional neural network, and hence the characters of license plates can be recognised successfully. Extensive experiments have been conducted, and the results indicate that LPRFH can recognise the license plates accurately even in some severe fog‐haze environments.
Segmentation of osteosarcoma in MRI images by K‐means clustering, Chan‐Vese segmentation, and iterative Gaussian filtering
Authors: Nasor Mohamed , Obaid Walid 
Published: December 2020

AbstractUnlike other types of tumours, automated osteosarcoma segmentation in magnetic resonance images (MRI) is a challenging task due to its different and unique intensity and texture. This paper presents a technique for segmenting osteosarcoma in MRI images using a combination of image processing techniques which include K‐means clustering, Chan‐Vese segmentation, iterative Gaussian filtering, and Canny edge detection. In addition, the proposed technique involves iterative morphological operations and object counting. The technique was tested using 50 MRI scan images that contain osteosarcoma tumours. The proposed technique was able to segment the osteosarcoma regardless of the variations in their intensities, textures and locations. The performance of the technique was measured by calculating the values for precision, recall, specificity, Dice score coefficient, accuracy and the running time (RT) for all tested cases. The proposed technique achieved 95.96% precision, 86.15% recall, 99.51% specificity, 89.84% Dice score coefficient, 98.02% accuracy, and 191.62 s average running time. This technique can assist clinicians in making treatment plans for patients with osteosarcoma.
High‐resolution optical‐to‐SAR image registration using mutual information and SPSA optimisation
Authors: Paul Sourabh , Pati Umesh C. 
Published: December 2020

AbstractThe high‐resolution optical and synthetic aperture radar (SAR) images are widely used in many remote sensing application areas such as image fusion and change detection where image registration is a fundamental step. The latest high‐resolution optical and SAR satellites and airborne systems provide geometrically corrected images which do not contain global deformations. Though the images do not have global differences, still, registration differences exist between these optical and SAR images. These registration differences should be minimised through an automatic registration method before using the images for the aforementioned applications. However, an automatic optical‐to‐SAR image registration is a challenging task due to the presence of significant nonlinear intensity differences as well as local geometric distortions between the images. In order to solve these problems, an automatic optical‐to‐SAR image registration method is proposed which can effectively handle the registration differences between the globally corrected high‐resolution images. In the proposed method, initially, a coarse registration is performed by using a discrete simultaneous perturbation stochastic approximation (SPSA) optimisation. Then, a smooth continuous SPSA optimisation is utilised for the fine registration of the images. Experiments are performed on six sets of high‐resolution optical‐SAR image pairs and the results show the effectiveness of the proposed method.
Characterizing the line emission from molecular clouds
Authors: Tafalla M. , Usero A. , Hacar A. 
Published: December 2020

Context. The traditional approach to characterize the structure of molecular clouds is to map their line emission.
Aims. We aim to test and apply a stratified random sampling technique that can characterize the line emission from molecular clouds more efficiently than mapping.
Methods. We sampled the molecular emission from the Perseus cloud using the H2 column density as a proxy. We divided the cloud into ten logarithmically spaced column density bins, and we randomly selected ten positions from each bin. The resulting 100 cloud positions were observed with the IRAM 30 m telescope, covering the 3 mm-wavelength band and parts of the 2 and 1 mm bands.
Results. We focus our analysis on the 11 molecular species (plus isotopologs) detected toward most column density bins. In all cases, the line intensity is tightly correlated with the H2 column density. For the CO isotopologs, the trend is relatively flat, while for high-dipole moment species such as HCN, CS, HCO+, and HNC, the trend is approximately linear. To reproduce this behavior, we developed a cloud model in which the gas density increases with column density, and where most species have abundance profiles characterized by an outer photodissociation edge and an inner freeze-out drop. With this model, we determine that the intensity behavior of the high-dipole moment species arises from a combination of excitation effects and molecular freeze out, with some modulation from optical depth. This quasi-linear dependence with the H2 column density makes the gas at low column densities dominate the cloud-integrated emission. It also makes the emission from most high-dipole moment species proportional to the cloud mass inside the photodissociation edge.
Conclusions. Stratified random sampling is an efficient technique for characterizing the emission from whole molecular clouds. When applied to Perseus, it shows that despite the complex appearance of the cloud, the molecular emission follows a relatively simple pattern. A comparison with available studies of whole clouds suggests that this emission pattern may be common.
The Variable Expression of a Novel MBD5 Gene Frameshift Mutation in an Italian Family
Authors: Orrico Alfredo , Galli Lucia , Rossi Maja , Cortesi Ambra , Mazzi Marta , Caterino Ettore 
Published: December 2020

AbstractHaploinsufficiency of the methyl-CpG-binding domain protein 5 (MBD5) gene is reported as a cause of an autosomal dominant type of cognitive disability (MRD1) and autism spectrum disorder through large deletions involving multiple genes or point mutations, ultimately leading to haploinsufficiency in both cases. However, relatively few reports have been published on the phenotypical spectrum resulting from point mutations.We report here on a novel heterozygous frameshift variant in the MBD5 gene [c.2579del; p.(Lys860Argfs*11)] in a family in which the typical signs associated with pathogenic variants were expressed with different degrees of severity in the clinical presentation of the carrier individuals.Our findings, adding a novel mutation to the mutational spectrum, further support the relevance of the MBD5 gene as one of the main molecular mechanisms involved in the pathogenesis of intellectual disability and contribute to the characterization of the genotype–phenotype correlations.
Minimally Invasive Approaches to Anterior Skull Base Meningiomas
Authors: Seaman Scott C. , Ali Muhammad S. , Marincovich Anthony , Li Luyuan , Walsh Jarrett E. , Greenlee Jeremy D. W. 
Published: December 2020

AbstractObjective Anterior skull base meningiomas include olfactory groove, planum sphenoidale, and tuberculum sellae lesions. Traditionally, standard craniotomy approaches have been used to access meningiomas in these locations. More recently, minimally invasive techniques including supraorbital and endonasal endoscopic approaches have gained favor; however there are limited published series comparing the use of these two techniques for these meningiomas. Using our patent database, we identified patients who underwent these two approaches, and conducted a retrospective chart review to compare outcomes between these two techniques.Methods A total of 32 patients who underwent minimally invasive approaches were identified: 20 supraorbital and 11 endoscopic endonasal. Radiographic images, presenting complaints and outcomes, were analyzed retrospectively. The safety of each approach was evaluated.Results The mean extent of resection through a supraorbital approach was significantly greater than that of the endoscopic endonasal approach, 88.1 vs. 57.9%, respectively (p = 0.016). Overall, preoperative visual acuity and anopsia deficits were more frequent in the endonasal group that persisted postoperatively (visual acuity: p = 0.004; anopsia: p = 0.011). No major complications including cerebrospinal fluid (CSF) leaks or wound-related complications were identified in the supraorbital craniotomy group, while the endonasal group had two CSF leaks requiring lumbar drain placement. Length of stay was shorter in the supraorbital group (3.4 vs. 6.1 days, p < 0.001).Conclusion Anterior skull base meningiomas can be successfully managed by both supraorbital and endoscopic endonasal approaches. Both approaches provide excellent direct access to tumor in carefully selected patients and are safe and efficient, but patient factors and symptoms should dictate the approach selected.
Does the Head Position Affect Neonatal Lateral Ventricular Volume?
Authors: Roy Priyanka , Lo Marcus , Bhattacharya Soume , Eagleson Roy , Fenster Aaron , de Ribaupierre Sandrine 
Published: December 2020

Objective This study aimed to determine whether there are differences in the lateral ventricular volumes, measured by three-dimensional ultrasound (3D US) depending on the posture of the neonate (right and left lateral decubitus).
Study Design This was a prospective analysis of the lateral ventricular volumes of preterm neonates recruited from Victoria Hospital, London, Ontario (June 2018–November 2019). A total of 24 premature neonates were recruited. The first cohort of 18 unstable premature neonates were imaged with 3D US in their current sides providing 15 right-sided and 16 left-sided 3D US images. The neonates in the second cohort of six relatively stable infants were imaged after positioning in each lateral decubitus position for 30 minutes, resulting in 40 3D US images obtained from 20 posture change sessions. The images were segmented and the ventricle volumes in each lateral posture were compared with determine whether the posture of the head influenced the volume of the upper and lower ventricle.
Results For the first cohort who did not have their posture changed, the mean of the right and left ventricle volumes were 23.81 ± 15.51 and 21.61 ± 16.19 cm3, respectively, for the 15 images obtained in a right lateral posture and 13.96 ± 8.69 and 14.92 ± 8.77 cm3, respectively, for the 16 images obtained in the left lateral posture. Similarly, for the second cohort who had their posture changed, the mean of right and left ventricle volumes were 20.92 ± 17.3 and 32.74 ± 32.33 cm3, respectively, after 30 minutes in the right lateral posture, and 21.25 ± 18.4 and 32.65 ± 31.58 cm3, respectively, after 30 minutes in the left lateral posture. Our results failed to show a statistically significant difference in ventricular volumes dependence on posture.
Conclusion Head positioned to any lateral side for 30 minutes does not have any effect on the lateral ventricular volumes of neonates.
Key Points