Three‐dimensional cellular imaging in thick biological tissue with confocal detection of one‐photon fluorescence in the near‐infrared II window

2019 ◽  
Vol 12 (7) ◽  
Author(s):  
Menghan Wang ◽  
Nanguang Chen
2015 ◽  
Vol 6 (1) ◽  
pp. 19-29 ◽  
Author(s):  
G. Bitelli ◽  
P. Conte ◽  
T. Csoknyai ◽  
E. Mandanici

The management of an urban context in a Smart City perspective requires the development of innovative projects, with new applications in multidisciplinary research areas. They can be related to many aspects of city life and urban management: fuel consumption monitoring, energy efficiency issues, environment, social organization, traffic, urban transformations, etc. Geomatics, the modern discipline of gathering, storing, processing, and delivering digital spatially referenced information, can play a fundamental role in many of these areas, providing new efficient and productive methods for a precise mapping of different phenomena by traditional cartographic representation or by new methods of data visualization and manipulation (e.g. three-dimensional modelling, data fusion, etc.). The technologies involved are based on airborne or satellite remote sensing (in visible, near infrared, thermal bands), laser scanning, digital photogrammetry, satellite positioning and, first of all, appropriate sensor integration (online or offline). The aim of this work is to present and analyse some new opportunities offered by Geomatics technologies for a Smart City management, with a specific interest towards the energy sector related to buildings. Reducing consumption and CO2 emissions is a primary objective to be pursued for a sustainable development and, in this direction, an accurate knowledge of energy consumptions and waste for heating of single houses, blocks or districts is needed. A synoptic information regarding a city or a portion of a city can be acquired through sensors on board of airplanes or satellite platforms, operating in the thermal band. A problem to be investigated at the scale A problem to be investigated at the scale of the whole urban context is the Urban Heat Island (UHI), a phenomenon known and studied in the last decades. UHI is related not only to sensible heat released by anthropic activities, but also to land use variations and evapotranspiration reduction. The availability of thermal satellite sensors is fundamental to carry out multi-temporal studies in order to evaluate the dynamic behaviour of the UHI for a city. Working with a greater detail, districts or single buildings can be analysed by specifically designed airborne surveys. The activity has been recently carried out in the EnergyCity project, developed in the framework of the Central Europe programme established by UE. As demonstrated by the project, such data can be successfully integrated in a GIS storing all relevant data about buildings and energy supply, in order to create a powerful geospatial database for a Decision Support System assisting to reduce energy losses and CO2 emissions. Today, aerial thermal mapping could be furthermore integrated by terrestrial 3D surveys realized with Mobile Mapping Systems through multisensor platforms comprising thermal camera/s, laser scanning, GPS, inertial systems, etc. In this way the product can be a true 3D thermal model with good geometric properties, enlarging the possibilities in respect to conventional qualitative 2D images with simple colour palettes. Finally, some applications in the energy sector could benefit from the availability of a true 3D City Model, where the buildings are carefully described through three-dimensional elements. The processing of airborne LiDAR datasets for automated and semi-automated extraction of 3D buildings can provide such new generation of 3D city models.


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Shanshan Chen ◽  
Zhiguang Liu ◽  
Huifeng Du ◽  
Chengchun Tang ◽  
Chang-Yin Ji ◽  
...  

AbstractKirigami, with facile and automated fashion of three-dimensional (3D) transformations, offers an unconventional approach for realizing cutting-edge optical nano-electromechanical systems. Here, we demonstrate an on-chip and electromechanically reconfigurable nano-kirigami with optical functionalities. The nano-electromechanical system is built on an Au/SiO2/Si substrate and operated via attractive electrostatic forces between the top gold nanostructure and bottom silicon substrate. Large-range nano-kirigami like 3D deformations are clearly observed and reversibly engineered, with scalable pitch size down to 0.975 μm. Broadband nonresonant and narrowband resonant optical reconfigurations are achieved at visible and near-infrared wavelengths, respectively, with a high modulation contrast up to 494%. On-chip modulation of optical helicity is further demonstrated in submicron nano-kirigami at near-infrared wavelengths. Such small-size and high-contrast reconfigurable optical nano-kirigami provides advanced methodologies and platforms for versatile on-chip manipulation of light at nanoscale.


Author(s):  
Ruiyuan Liu ◽  
Yuping Zhou ◽  
Di Zhang ◽  
Genghan He ◽  
Chuang Liu ◽  
...  

Design and synthesis of near-infrared (NIR) emissive fluorophore for imaging of organelle and photodynamic therapy has received enormous attention. Hence, NIR emissive fluorophore of high-fidelity lysosome targeting, two-photon fluorescence imaging,...


2021 ◽  
Author(s):  
Ziang Guo ◽  
Xiaowei Huang ◽  
Zhihua Li ◽  
Jiyong Shi ◽  
Xuetao Hu ◽  
...  

This paper describes a Near-infrared quantum dots (CuInS2 QDs)/antibiotics (vancomycin) nanoparticle-based assay for Staphylococcus aureus and iron(Ⅲ) detection. CuInS2 QDs with good biological tissue permeability and biocompatibility are combined with...


2019 ◽  
Vol 12 (06) ◽  
pp. 1950012 ◽  
Author(s):  
Hequn Zhang ◽  
Weisi Xie ◽  
Ming Chen ◽  
Liang Zhu ◽  
Zhe Feng ◽  
...  

Rodents are popular biological models for physiological and behavioral research in neuroscience and rats are better models than mice due to their higher genome similarity to human and more accessible surgical procedures. However, rat brain is larger than mice brain and it needs powerful imaging tools to implement better penetration against the scattering of the thicker brain tissue. Three-photon fluorescence microscopy (3PFM) combined with near-infrared (NIR) excitation has great potentials for brain circuits imaging because of its abilities of anti-scattering, deep-tissue imaging, and high signal-to-noise ratio (SNR). In this work, a type of AIE luminogen with red fluorescence was synthesized and encapsulated with Pluronic F-127 to make up form nanoparticles (NPs). Bright DCDPP-2TPA NPs were employed for in vivo three-photon fluorescent laser scanning microscopy of blood vessels in rats brain under 1550[Formula: see text]nm femtosecond laser excitation. A fine three-dimensional (3D) reconstruction up to the deepness of 600[Formula: see text][Formula: see text]m was achieved and the blood flow velocity of a selected vessel was measured in vivo as well. Our 3PFM deep brain imaging method simultaneously recorded the morphology and function of the brain blood vessels in vivo in the rat model. Using this angiography combined with the arsenal of rodent’s brain disease, models can accelerate the neuroscience research and clinical diagnosis of brain disease in the future.


Nano Letters ◽  
2021 ◽  
Author(s):  
Rachel Langenbacher ◽  
Januka Budhathoki-Uprety ◽  
Prakrit V. Jena ◽  
Daniel Roxbury ◽  
Jason Streit ◽  
...  

Author(s):  
Jun-Li Xu ◽  
Cecilia Riccioli ◽  
Ana Herrero-Langreo ◽  
Aoife Gowen

Deep learning (DL) has recently achieved considerable successes in a wide range of applications, such as speech recognition, machine translation and visual recognition. This tutorial provides guidelines and useful strategies to apply DL techniques to address pixel-wise classification of spectral images. A one-dimensional convolutional neural network (1-D CNN) is used to extract features from the spectral domain, which are subsequently used for classification. In contrast to conventional classification methods for spectral images that examine primarily the spectral context, a three-dimensional (3-D) CNN is applied to simultaneously extract spatial and spectral features to enhance classificationaccuracy. This tutorial paper explains, in a stepwise manner, how to develop 1-D CNN and 3-D CNN models to discriminate spectral imaging data in a food authenticity context. The example image data provided consists of three varieties of puffed cereals imaged in the NIR range (943–1643 nm). The tutorial is presented in the MATLAB environment and scripts and dataset used are provided. Starting from spectral image pre-processing (background removal and spectral pre-treatment), the typical steps encountered in development of CNN models are presented. The example dataset provided demonstrates that deep learning approaches can increase classification accuracy compared to conventional approaches, increasing the accuracy of the model tested on an independent image from 92.33 % using partial least squares-discriminant analysis to 99.4 % using 3-CNN model at pixel level. The paper concludes with a discussion on the challenges and suggestions in the application of DL techniques for spectral image classification.


RSC Advances ◽  
2015 ◽  
Vol 5 (99) ◽  
pp. 80929-80932 ◽  
Author(s):  
Xiaofang Jia ◽  
Dan Li ◽  
Jing Li ◽  
Erkang Wang

Water-dispersed ultrasmall Ag2S nanoclusters (NCs) are prepared directly in aqueous phase via a facile microwave synthesis. The resultant Ag2S NCs with tunable emission from visible to NIR region are promising biological probes for cellular imaging.


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