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Extracellular Vesicles: Biology and Potentials in Cancer Therapeutics
| Extracellular Vesicles: Biology and Potentials in Cancer Therapeutics |
| Autore | Cho William |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 electronic resource (394 p.) |
| Soggetto topico | Medicine |
| Soggetto non controllato |
extracellular vesicle
precision oncology cancer biomarker prostate cancer drug delivery extracellular vesicles lysosome nanocarriers ultrasound size exclusion chromatography differential ultracentrifugation head and neck squamous cell carcinoma (HNSCC) exosomes cancer biomarker diagnostic therapy liquid biopsy small extracellular vesicles (sEV) tumor-derived exosomes (TEX) melanoma cell-derived exosomes (MTEX) proteomics tumor microenvironment biomarkers ectosomes neoplasia microvesicles small extracellular vesicles isolation purification size-exclusion chromatography ultracentrifugation sucrose density cushion lymph node spleen solid tissue microvesicle exosome cancer therapeutic drug carrier flow cytometry immunophenotyping swarm detection tumor-associated macrophages macrophage polarization mannose receptor HIV-1 Nef glioblastoma microRNA immunoprecipitation CD44 human milk nutrient microbiota microRNAs nanocommunicator diagnostic biomarker drug delivery vehicle personalized cancer immunotherapy therapeutic agents cell-to-cell communication ionising radiation non-targeted effects signalling imaging flow cytometry biomarker reservoirs cancer diagnostics disease monitoring large EVs ovarian cancer cells ES-2 OAW-42 adipose tissue origin mesenchymal stem cells |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | Extracellular Vesicles |
| Record Nr. | UNINA-9910557555303321 |
Cho William
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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UAVs for Vegetation Monitoring
| UAVs for Vegetation Monitoring |
| Autore | de Castro Megías Ana |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 electronic resource (452 p.) |
| Soggetto topico | Research & information: general |
| Soggetto non controllato |
UAS
UAV vegetation cover multispectral land cover forest Acacia Indonesia tropics vegetation ground cover vegetation indices agro-environmental measures olive groves southern Spain sUAS water stress ornamental container-grown artificial intelligence machine learning deep learning neural network visual recognition precision agriculture canopy cover image analysis crop mapping evapotranspiration (ET) GRAPEX remote sensing Two Source Energy Balance model (TSEB) contextual spatial domain/resolution data aggregation eddy covariance (EC) Fusarium wilt crop disease banana multispectral remote sensing purple rapeseed leaves unmanned aerial vehicle U-Net plant segmentation nitrogen stress Glycine max RGB canopy height close remote sensing growth model curve fitting NDVI solar zenith angle flight altitude time of day operating parameters CNN Faster RCNN SSD Inception v2 patch-based CNN MobileNet v2 detection performance inference time disease detection cotton root rot plant-level single-plant plant-by-plant classification UAV remote sensing crop monitoring RGB imagery multispectral imagery century-old biochar semantic segmentation random forest crop canopy multispectral image chlorophyll content remote sensing technique individual plant segmentation plant detection transfer learning maize tassel tassel branch number convolution neural network VGG16 plant nitrogen estimation vegetation index image segmentation transpiration method comparison oil palm multiple linear regression support vector machine artificial neural network UAV hyperspectral wheat yellow rust disease monitoring texture spatial resolution RGB camera thermal camera drought tolerance forage grass HSV CIELab broad-sense heritability phenotyping gap high throughput field phenotyping UAV digital images winter wheat biomass multiscale textures red-edge spectra least squares support vector machine variable importance drone hyperspectral thermal nutrient deficiency weed detection disease diagnosis plant trails |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557661103321 |
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de Castro Megías Ana
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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