LEADER 01387nam 2200469 450 001 9910677115903321 005 20210610073738.0 010 $a1-119-61110-5 010 $a1-119-53842-4 010 $a1-119-53856-4 035 $a(CKB)4100000009184406 035 $a(MiAaPQ)EBC5890646 035 $a(EXLCZ)994100000009184406 100 $a20191001d2020 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aBioelectrochemical interface engineering /$fedited by Dr. R. Navanietha Krishnaraj, Dr. Rajesh K. Sani 210 1$aHoboken, New Jersey ;$aChichester, West Sussex, England :$cWiley,$d[2020] 210 4$d©2020 215 $a1 online resource (559 pages) 311 $a1-119-53854-8 606 $aBioelectrochemistry 606 $aBioengineering 606 $aChemical engineering 615 0$aBioelectrochemistry. 615 0$aBioengineering. 615 0$aChemical engineering. 676 $a572.437 700 $aKrishnaraj$b R. Navanietha$01340941 702 $aNavanietha Krishnaraj$b R. 702 $aSani$b Rajesh K. 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 801 2$bAzTeS 906 $aBOOK 912 $a9910677115903321 996 $aBioelectrochemical interface engineering$93063149 997 $aUNINA LEADER 04286nam 22005293 450 001 9910688365203321 005 20231110223914.0 035 $a(CKB)5590000000535774 035 $a(MiAaPQ)EBC6578100 035 $a(Au-PeEL)EBL6578100 035 $a(OCoLC)1237001729 035 $a(NjHacI)995590000000535774 035 $a(PPN)25628931X 035 $a(Perlego)3801628 035 $a(EXLCZ)995590000000535774 100 $a20220207d2020 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAgroecology 210 1$aVersailles :$cQuae,$d2020. 210 4$d©2021. 215 $a1 online resource (105 pages) 225 1 $aMatière à débattre et Décider 311 08$a9782759232956 311 08$a2759232956 327 $aIntro Contents Foreword Introduction Founding principles National and international societal expectations Research based on new paradigms and new approaches References 1. Integrating agroecology into agri-food systems Products resulting from agroecology and their properties Actors' strategies Spatial organization of markets Dynamics and coherence of the agri-food system Research questions References 2. The agroecological transition of farms Recent scientific advances Some examples Research questions Developing the necessary transdisciplinarity References 3. Leveraging regulation processes in multifunctional landscapes Recent scientific advances Some examples Research questions References 4. Leveraging genetic diversity in plant and animal breeding Recent scientific advances Some examples Research questions References 5. Modelling interactions between living organisms in their environments and socio-economic contexts Recent scientific advances Some examples Research issues References 6. Contribution of agricultural equipment and digital technology to agroecology: considering living organisms better Recent scientific advances Some examples Research questions References Conclusions Diversity and diversification: observe, translate, direct Fro m massive acquisition of biological data to new types of experiments Und erstanding risk and uncertainty: modelling and sharing of experiences Scaling up and changing agri-food organization for agroecology Outlook Contributors. 330 $aAgroecology was chosen by INRAE as one of its interdisciplinary scientific foresight studies designed to identify research fronts in response to major societal challenges. Eighty researchers drew up an assessment and proposed research avenues for agroecology. This book summarizes their main conclusions. Agroecology, as a scientific discipline that puts ecology back at the centre of agricultural system design, is now well established. Diversification of living organisms in agroecosystems is a broad objective that is intended to make these systems more robust and resilient. Research in genetics and landscape ecology must be mobilized so that agroecology can use mechanisms from the field to landscape scales. Progress is being made in modelling agroecological systems to better understand the many biotic and abiotic interactions, to predict them, and to begin to manage some of them. Diversification of living organisms in agricultural production (species, varieties, crop rotations, etc.) leads to more varied products. The consequences will be significant on the commodity chains, and more precisely on agri-food systems, from production methods to product consumption. These changes are long-term. The agroecological transition, which is adaptive, co-constructed with all actors, is in itself a research subject, and will rely on experimental devices, farms, and 'Territories of innovation'. 410 0$aMatière à débattre et Décider 517 $aAgroecology 606 $aAgricultural ecology 606 $aAgronomy 615 0$aAgricultural ecology. 615 0$aAgronomy. 676 $a577.55 700 $aCaquet$b Thierry$01075821 701 $aGascuel$b Chantal$01075822 701 $aTixier-Boichard$b Michèle$01075823 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910688365203321 996 $aAgroecology$92585788 997 $aUNINA LEADER 07896nam 22007815 450 001 9910635394903321 005 20251225202023.0 010 $a9783031210143 010 $a303121014X 024 7 $a10.1007/978-3-031-21014-3 035 $a(MiAaPQ)EBC7158099 035 $a(Au-PeEL)EBL7158099 035 $a(CKB)25732560500041 035 $a(DE-He213)978-3-031-21014-3 035 $a(PPN)268663750 035 $a(EXLCZ)9925732560500041 100 $a20221215d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine Learning in Medical Imaging $e13th International Workshop, MLMI 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings /$fedited by Chunfeng Lian, Xiaohuan Cao, Islem Rekik, Xuanang Xu, Zhiming Cui 205 $a1st ed. 2022. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2022. 215 $a1 online resource (491 pages) 225 1 $aLecture Notes in Computer Science,$x1611-3349 ;$v13583 311 08$aPrint version: Lian, Chunfeng Machine Learning in Medical Imaging Cham : Springer,c2023 9783031210136 320 $aIncludes bibliographical references and index. 327 $aFunction MRI Representation Learning via Self-Supervised Transformer for Automated Brain Disorder Analysis -- Predicting Age-related Macular Degeneration Progression with Longitudinal Fundus Images using Deep Learning -- Region-Guided Channel-Wise Attention Network for Accelerated MRI Reconstruction -- Student Becomes Decathlon Master in Retinal Vessel Segmentation via Dual-teacher Multi-target Domain Adaptation -- Rethinking Degradation: Radiograph Super-Resolution via AID-SRGAN -- 3D Segmentation with Fully Trainable Gabor Kernels and Pearson's Correlation Coefficient -- A More Design-flexible Medical Transformer for Volumetric Image Segmentation -- Dcor-VLDet: A Vertebra Landmark Detection Network for Scoliosis Assessment with Dual Coordinate System -- Plug-and-play Shape Refinement Framework for Multi-site and Lifespan Brain Skull Stripping -- A Coarse-To-Fine Network for Craniopharyngioma Segmentation -- Patch-level instance-group discrimination with pretext-invariant learning for colitis scoring -- AutoMO-Mixer: An automated multi-objective Mixer model for balanced, safe and robust prediction in medicine -- Memory transformers for full context and high-resolution 3D Medical Segmentation -- Whole Mammography Diagnosis via Multi-instance Supervised Discriminative Localization and Classification -- Cross Task Temporal Consistency for Semi Supervised Medical Image Segmentation -- U-Net vs Transformer: Is U-Net Outdated in Medical Image Registration -- UNet-eVAE: Iterative refinement using VAE embodied learning for endoscopic image segmentation -- Dynamic Linear Transformer for 3D Biomedical Image Segmentation -- Automatic Grading of Emphysema by Combining 3D Lung Tissue Appearance and Deformation Map Using a Two-stream Fully Convolutional Neural Network -- A Novel Two-Stage Multi-View Low-Rank Sparse Subspace Clustering Approach to Explore the Relationship between Brain Function and Structure -- Fast Image-Level MRI Harmonization via Spectrum Analysis -- CT2CXR: CT-based CXR Synthesis for Covid-19 Pneumonia Classification -- Harmonization of Multi-Site Cortical Data Across the Human Lifespan -- Head and neck vessel segmentation with connective topology using affinity graph -- Coarse Retinal Lesion Annotations Refinement via Prototypical Learning -- Nuclear Segmentation and Classification: On Color & Compression Generalization -- Understanding Clinical Progression of Late-Life Depression to Alzheimer?s Disease Over 5 Years with Structural MRI -- ClinicalRadioBERT: Knowledge-Infused Few Shot Learning for Clinical Notes Named Entity Recognition -- Graph Representation Neural Architecture Search for Optimal Spatial/Temporal Functional Brain Network Decomposition -- Driving Points Prediction For Abdominal Probabilistic Registration -- CircleSnake: Instance Segmentation with Circle Representation -- Vertebrae localization, segmentation and identification using a graph optimization and an anatomic consistency cycle -- Coronary Ostia Localization Using Residual U-Net with HeatmapMatching and 3D DSNT -- AMLP-Conv, a 3D Axial Long-range Interaction Multilayer Perceptron for CNNs -- Neural State-Space Modeling with Latent Causal-Effect Disentanglement -- Adaptive Unified Contrastive Learning for Imbalanced Classification -- Prediction of HPV-Associated Genetic Diversity for Squamous Cell Carcinoma of Head and Neck Cancer based on 18F-FDG PET/CT -- TransWS: Transformer-based Weakly Supervised Histology Image Segmentation -- Contextual Attention Network: Transformer Meets U-Net -- Intelligent Masking: Deep Q-Learning for Context Encoding in Medical Image Analysis -- A New Lightweight Architecture and a Class Imbalance Aware Loss Function for Multi-label Classification of Intracranial Hemorrhages -- Spherical Transformer on Cortical Surfaces -- Accurate localization of inner ear regions of interests using deep reinforcement learning -- Shifted Windows Transformers for Medical Image Quality Assessment -- Multi-scale Multi-structure Siamese Network (MMSNet) for Primary Open-angleGlaucoma Prediction -- HealNet - Self-Supervised Acute Wound Heal-Stage Classification -- Federated Tumor Segmentation with Patch-wise Deep Learning Model -- Multi-scale and Focal Region Based Deep Learning Network for Fine Brain Parcellation. 330 $aThis book constitutes the proceedings of the 13th International Workshop on Machine Learning in Medical Imaging, MLMI 2022, held in conjunction with MICCAI 2022, in Singapore, in September 2022. The 48 full papers presented in this volume were carefully reviewed and selected from 64 submissions. They focus on major trends and challenges in the above-mentioned area, aiming to identify new-cutting-edge techniques and their uses in medical imaging. Topics dealt with are: deep learning, generative adversarial learning, ensemble learning, sparse learning, multi-task learning, multi-view learning, manifold learning, and reinforcement learning, with their applications to medical image analysis, computer-aided detection and diagnosis, multi-modality fusion, image reconstruction, image retrieval, cellular image analysis, molecular imaging, digital pathology, etc. 410 0$aLecture Notes in Computer Science,$x1611-3349 ;$v13583 606 $aComputer vision 606 $aPattern recognition systems 606 $aMachine learning 606 $aComputer engineering 606 $aComputer networks 606 $aSocial sciences$xData processing 606 $aBioinformatics 606 $aComputer Vision 606 $aAutomated Pattern Recognition 606 $aMachine Learning 606 $aComputer Engineering and Networks 606 $aComputer Application in Social and Behavioral Sciences 606 $aComputational and Systems Biology 615 0$aComputer vision. 615 0$aPattern recognition systems. 615 0$aMachine learning. 615 0$aComputer engineering. 615 0$aComputer networks. 615 0$aSocial sciences$xData processing. 615 0$aBioinformatics. 615 14$aComputer Vision. 615 24$aAutomated Pattern Recognition. 615 24$aMachine Learning. 615 24$aComputer Engineering and Networks. 615 24$aComputer Application in Social and Behavioral Sciences. 615 24$aComputational and Systems Biology. 676 $a929.605 676 $a006.37 702 $aLian$b Chunfeng 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910635394903321 996 $aMachine Learning in Medical Imaging$92998079 997 $aUNINA