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Until recently, a majority of the applications of X-ray computed tomography (CT) scanning in plant sciences remained descriptive; some included a quantification of the plant materials when the root-soil isolation or branch-leaf separation was satisfactory; and a few involved the modeling of plant biology processes or the assessment of treatment or disease effects on plant biomass and structures during growth. In the last decade, repeated CT scanning of the same plants was reported in an increasing number of studies in which moderate doses of X-rays had been used. Besides the general objectives of Frontiers in Plant Science research topics, “Branching and Rooting Out with a CT Scanner” was proposed to meet specific objectives: (i) providing a non-technical update on knowledge about the application of CT scanning technology to plants, starting with the type of CT scanning data collected (CT images vs. CT numbers) and their processing in the graphical and numerical approaches; (ii) drawing the limits of the CT scanning approach, which because it is based on material density can distinguish materials with contrasting or moderately overlapping densities (e.g., branches vs. leaves, roots vs. non-organic soils) but not the others (e.g., roots vs. organic soils); (iii) explaining with a sufficient level of detail the main procedures used for graphical, quantitative and statistical analyses of plant CT scanning data, including fractal complexity measures and statistics appropriate for repeated plant CT scanning, in experiments where the research hypotheses are about biological processes such as light interception by canopies, root disease development and plant growth under stress conditions; (iv) comparing plant CT scanning with an alternative technology that applies to plants, such as the phenomics platforms which target leaf canopies; and (v) providing current and potential users of plant CT scanning with up-to-date information and exhaustive documentation, including clear perspectives and well-defined goals for the future, for them to be even more efficient or most efficient from start in their research work.

plant CT scanning data collection and analysis --- phytopathological and environmental stress applications --- plant imaging and phenotyping --- plant structural complexity and fractal geometry --- appropriate statistical methods for plant data --- Computed tomography (CT)

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The goal of the Special Issue “Brittle Materials in Mechanical Extremes” is to spark a discussion of the analogies and the differences between different brittle materials, such as ceramics and concrete. The contributions to the Issue span from construction materials (asphalt and concrete) to structural ceramics to ice. Data reported in the Issue were obtained by advanced microstructural techniques (microscopy, 3D imaging, etc.) and linked to mechanical properties (and their changes as a function of aging, composition, etc.). The description of the mechanical behavior of brittle materials under operational loads, for instance, concrete and ceramics under very high temperatures, offers an unconventional viewpoint on the behavior of such materials. While it is by no means exhaustive, this Special Issue paves the road for the fundamental understanding and further development of materials.

restraint --- creep --- double feedback method --- concrete --- temperature stress testing machine (TSTM) --- alkali-activated slag --- elevated temperatures --- Na2O concentration --- residual strength --- brittleness --- melting --- Fiber-reinforced concrete --- X-ray computed tomography (CT) --- anisotropic fiber orientation --- inverse analysis --- silica --- super-insulating materials --- instrumented indentation --- porosity --- electro-fused zirconia --- microcracking --- synchrotron x-ray refraction radiography (SXRR) --- thermal expansion --- ice --- high rate loading --- compressive loading --- Split Hopkinson bar --- in-situ fractography --- biomaterials --- bioceramics --- coating --- mechanical properties --- existing buildings --- reinforced concrete --- seismic vulnerability assessment --- in situ concrete strength --- variability of concrete strength --- high speed railway --- SBS/CR modified asphalt --- long-term aging --- anti-aging --- engineered cementitious composites --- steel grid --- fiber --- tensile capacity --- energy dissipation --- ceramics --- asphalt --- microstructure --- strength

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In a similar way to many other engineering fields, the road pavement industry strongly affects the critical issues of our generation, including climate change, pollutant emission, the exploitation of natural resources and economic crises. For this reason, technicians and researchers are searching ravenously for sustainable solutions to implement in current road construction systems with the following goals: To reduce the consumption of energy and virgin materials; To run environmentally and economically friendly maintenance; To recycle waste from different industrial processes; To decrease the noise, the pollution and the heat generated by traffic, particularly in urban contexts. This Special Issue aims to collect high-quality studies that combine the aforementioned solutions, including works pertaining to: The hot, warm, and cold recycling of reclaimed asphalt pavement; Marginal materials for asphalt pavements; Innovative sustainable materials; Durability and environmental aspects; Structure performance, modeling and design; Advanced trends in rehabilitation and preservation; Surface characteristics and road safety; Management system/life cycle analysis; Urban heat island mitigation; Energy harvesting.

porous concrete --- metakaolin --- geopolymers --- permeable pavements --- urban drainage systems --- maintenance --- reinforced asphalt pavement --- geogrid --- interlayer bonding --- static shear test --- cyclic shear test --- fatigue properties --- warm mix asphalt --- natural zeolite --- gas emissions --- energy consumption --- production costs --- bituminous mixtures --- nano-additives --- nanoclay --- carbon nanotubes --- graphene nanoplatelets --- nano-calcium oxide --- nano-titanium dioxide --- sonication --- fatigue performance --- self-healing --- hot-mix asphalt --- ageing --- cooling --- temperature segregation --- hauling --- insulated truck --- re-heating --- contact stresses --- rolling resistance --- braking --- free rolling --- load --- inflation pressure --- speed --- porosity --- permeability coefficients --- mixing ratio --- aggregate size --- compressive strength --- computed tomography (CT) image --- ex-post CBA --- road modernisation --- incidence of traffic accidents --- decision-making process --- life cycle assessment --- waste management --- circular economy --- alternative materials --- construction --- road stabilisation --- bearing capacity --- unbound base course --- cold central-plant recycled base course --- falling weight deflectometer (FWD), cold recycling in-plant --- bitumen --- aging --- rejuvenation --- reclaimed asphalt --- recycling --- coal bottom ash --- waste material --- recycle --- construction industry --- civil engineering --- bitumen selection --- performance-graded bitumen --- asphalt pavement temperatures --- temperature maps --- n/a

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In a similar way to many other engineering fields, the road pavement industry strongly affects the critical issues of our generation, including climate change, pollutant emission, the exploitation of natural resources and economic crises. For this reason, technicians and researchers are searching ravenously for sustainable solutions to implement in current road construction systems with the following goals: To reduce the consumption of energy and virgin materials; To run environmentally and economically friendly maintenance; To recycle waste from different industrial processes; To decrease the noise, the pollution and the heat generated by traffic, particularly in urban contexts. This Special Issue aims to collect high-quality studies that combine the aforementioned solutions, including works pertaining to: The hot, warm, and cold recycling of reclaimed asphalt pavement; Marginal materials for asphalt pavements; Innovative sustainable materials; Durability and environmental aspects; Structure performance, modeling and design; Advanced trends in rehabilitation and preservation; Surface characteristics and road safety; Management system/life cycle analysis; Urban heat island mitigation; Energy harvesting.

Technology: general issues --- History of engineering & technology --- porous concrete --- metakaolin --- geopolymers --- permeable pavements --- urban drainage systems --- maintenance --- reinforced asphalt pavement --- geogrid --- interlayer bonding --- static shear test --- cyclic shear test --- fatigue properties --- warm mix asphalt --- natural zeolite --- gas emissions --- energy consumption --- production costs --- bituminous mixtures --- nano-additives --- nanoclay --- carbon nanotubes --- graphene nanoplatelets --- nano-calcium oxide --- nano-titanium dioxide --- sonication --- fatigue performance --- self-healing --- hot-mix asphalt --- ageing --- cooling --- temperature segregation --- hauling --- insulated truck --- re-heating --- contact stresses --- rolling resistance --- braking --- free rolling --- load --- inflation pressure --- speed --- porosity --- permeability coefficients --- mixing ratio --- aggregate size --- compressive strength --- computed tomography (CT) image --- ex-post CBA --- road modernisation --- incidence of traffic accidents --- decision-making process --- life cycle assessment --- waste management --- circular economy --- alternative materials --- construction --- road stabilisation --- bearing capacity --- unbound base course --- cold central-plant recycled base course --- falling weight deflectometer (FWD), cold recycling in-plant --- bitumen --- aging --- rejuvenation --- reclaimed asphalt --- recycling --- coal bottom ash --- waste material --- recycle --- construction industry --- civil engineering --- bitumen selection --- performance-graded bitumen --- asphalt pavement temperatures --- temperature maps --- porous concrete --- metakaolin --- geopolymers --- permeable pavements --- urban drainage systems --- maintenance --- reinforced asphalt pavement --- geogrid --- interlayer bonding --- static shear test --- cyclic shear test --- fatigue properties --- warm mix asphalt --- natural zeolite --- gas emissions --- energy consumption --- production costs --- bituminous mixtures --- nano-additives --- nanoclay --- carbon nanotubes --- graphene nanoplatelets --- nano-calcium oxide --- nano-titanium dioxide --- sonication --- fatigue performance --- self-healing --- hot-mix asphalt --- ageing --- cooling --- temperature segregation --- hauling --- insulated truck --- re-heating --- contact stresses --- rolling resistance --- braking --- free rolling --- load --- inflation pressure --- speed --- porosity --- permeability coefficients --- mixing ratio --- aggregate size --- compressive strength --- computed tomography (CT) image --- ex-post CBA --- road modernisation --- incidence of traffic accidents --- decision-making process --- life cycle assessment --- waste management --- circular economy --- alternative materials --- construction --- road stabilisation --- bearing capacity --- unbound base course --- cold central-plant recycled base course --- falling weight deflectometer (FWD), cold recycling in-plant --- bitumen --- aging --- rejuvenation --- reclaimed asphalt --- recycling --- coal bottom ash --- waste material --- recycle --- construction industry --- civil engineering --- bitumen selection --- performance-graded bitumen --- asphalt pavement temperatures --- temperature maps

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The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis.

interpretable/explainable machine learning --- image classification --- image processing --- machine learning models --- white box --- black box --- cancer prediction --- deep learning --- multimodal learning --- convolutional neural networks --- autism --- fMRI --- texture analysis --- melanoma --- glcm matrix --- machine learning --- classifiers --- explainability --- explainable AI --- XAI --- medical imaging --- diagnosis --- ARMD --- change detection --- unsupervised learning --- microwave breast imaging --- image reconstruction --- tumor detection --- digital pathology --- whole slide image processing --- multiple instance learning --- deep learning classification --- HER2 --- medical images --- transfer learning --- optimizers --- neo-adjuvant treatment --- tumour cellularity --- cancer --- breast cancer --- diagnostics --- imaging --- computation --- artificial intelligence --- 3D segmentation --- active surface --- discriminant analysis --- PET imaging --- medical image analysis --- brain tumor --- cervical cancer --- colon cancer --- lung cancer --- computer vision --- musculoskeletal images --- lung disease detection --- taxonomy --- convolutional neural network --- CycleGAN --- data augmentation --- dermoscopic images --- domain transfer --- macroscopic images --- skin lesion segmentation --- infection detection --- COVID-19 --- X-ray images --- bayesian inference --- shifted-scaled dirichlet distribution --- MCMC --- gibbs sampling --- object detection --- surgical tools --- open surgery --- egocentric camera --- computers in medicine --- segmentation --- MRI --- ECG signal detection --- portable monitoring devices --- 1D-convolutional neural network --- medical image segmentation --- domain adaptation --- meta-learning --- U-Net --- computed tomography (CT) --- magnetic resonance imaging (MRI) --- low-dose --- sparse-angle --- quantitative comparison --- interpretable/explainable machine learning --- image classification --- image processing --- machine learning models --- white box --- black box --- cancer prediction --- deep learning --- multimodal learning --- convolutional neural networks --- autism --- fMRI --- texture analysis --- melanoma --- glcm matrix --- machine learning --- classifiers --- explainability --- explainable AI --- XAI --- medical imaging --- diagnosis --- ARMD --- change detection --- unsupervised learning --- microwave breast imaging --- image reconstruction --- tumor detection --- digital pathology --- whole slide image processing --- multiple instance learning --- deep learning classification --- HER2 --- medical images --- transfer learning --- optimizers --- neo-adjuvant treatment --- tumour cellularity --- cancer --- breast cancer --- diagnostics --- imaging --- computation --- artificial intelligence --- 3D segmentation --- active surface --- discriminant analysis --- PET imaging --- medical image analysis --- brain tumor --- cervical cancer --- colon cancer --- lung cancer --- computer vision --- musculoskeletal images --- lung disease detection --- taxonomy --- convolutional neural network --- CycleGAN --- data augmentation --- dermoscopic images --- domain transfer --- macroscopic images --- skin lesion segmentation --- infection detection --- COVID-19 --- X-ray images --- bayesian inference --- shifted-scaled dirichlet distribution --- MCMC --- gibbs sampling --- object detection --- surgical tools --- open surgery --- egocentric camera --- computers in medicine --- segmentation --- MRI --- ECG signal detection --- portable monitoring devices --- 1D-convolutional neural network --- medical image segmentation --- domain adaptation --- meta-learning --- U-Net --- computed tomography (CT) --- magnetic resonance imaging (MRI) --- low-dose --- sparse-angle --- quantitative comparison