Near-infrared Channels Research Articles

A Triple-Responsive and Dual-NIR Emissive Fluorescence Probe for Precise Cancer Imaging and Therapy by Activating Pyroptosis Pathway.

Revealing changes in the tumor microenvironment is crucial for understanding cancer and developing sensitive methods for precise cancer imaging and diagnosis. Intracellular hydrogen peroxide (H2O2) and microenvironmental factors (e.g., viscosity and polarity) are closely linked to various physiological and pathological processes, making them potential biomarkers for cancer. However, a triple-response theranostic probe for precise tumor imaging and therapy has not yet been achieved due to the lack of effective tools. Herein, we present a mitochondria-targeting near-infrared (NIR) fluorescent probe, VPH-5DF, capable of simultaneously monitoring H2O2, viscosity, and polarity through dual NIR channels. The probe specifically detects H2O2 via NIR emission (λem = 650 nm) and shows high sensitivity to microenvironmental viscosity/polarity in the deep NIR channel (λem ≈ 750 nm). Furthermore, the probe not only monitors mitochondrial polarity, viscosity, and fluctuations in endogenous/exogenous H2O2 levels but also distinguishes cancer cells from normal cells through multiple parameters. Additionally, VPH-5DF can be employed to monitor alterations in H2O2 levels, as well as changes in viscosity and polarity, during drug-induced pyroptosis in living cells. After treatment with VPH-5DF, chemotherapy-induced oxidative damage to the mitochondria in tumor cells activated the pyroptosis pathway, leading to a robust antitumor response, as evidenced in xenograft tumor models. Thus, this triple-response theranostic prodrug offers a new platform for precise in vivo cancer diagnosis and anticancer chemotherapy.

Calibration of MAJIS (Moons and Jupiter Imaging Spectrometer): V. Validation with mineral samples and reference materials.

MAJIS (Moons and Jupiter Imaging Spectrometer) is the imaging spectrometer onboard ESA's JUICE (JUpiter and ICy Moons Explorer) spacecraft that operates in the visible and near/mid-infrared between 0.5 and 5.54μm. Before the launch of JUICE in April 2023, MAJIS underwent a comprehensive on-ground calibration campaign in between August and September 2021 in the IAS (Institut d'Astrophysique Spatiale, Université Paris-Saclay) calibration facilities. Among all the operations, calibration sequences using a set of natural mineral samples and synthetic reference materials were acquired in order to characterize MAJIS performances under conditions assumed to be close to certain future observation configurations. Here, we analyze these calibration measurements using comparison with laboratory reference spectra to quantify MAJIS spectral and spatial performances while observing these solid surfaces. We first assess the MAJIS absolute spectral calibration of the visible and near-infrared channel covering half of the wavelength range. We then quantify spectral performances in terms of global spectral slopes, band detection, band shape, and depth retrievals, over most of the spectral range using six mineral samples. We conclude that for most configurations, the MAJIS instrument demonstrates excellent spectral performances compliant with the requirements. MAJIS can, however, be affected by stray light contributions, notably for wavelengths lower than about 1.2μm, and some performances of the instrument may then be significantly impacted depending on viewing conditions. In particular, we have identified cases of spectral contrast reduction up to 40%, absolute spectral shifts up to 2-3nm, and spectral smile variability by +/1nm. Finally, we used the MAJIS internal scanning mirror to test its ability to construct hyperspectral images of a few samples: we present the first band depth maps derived with MAJIS while observing a serpentine/carbonate sample, as well as an evaluation of MAJIS spatial point spread function. Overall, the analysis of MAJIS behavior while observing samples confirms most MAJIS expected performance requirements, while revealing subtle spectral perturbations that may be related to stray light and viewing conditions. These differences will be further investigated in-flight during the cruise, with a solar reflected target such as the Moon, as well as Jupiter before the JUICE orbital insertion.

Early polar-orbiting satellite-based fire remote sensing during the 1960s

ABSTRACT Satellite-based sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) now routinely provide a detailed picture of global fire activity on a daily basis. These systems explicitly included biomass burning as a phenomenon of interest, and their design was heavily influenced by experience gained with the comparatively primitive Advanced Very High Resolution Radiometer (AVHRR) first deployed on the TIROS-N satellite in the late 1970s. The AVHRR is generally recognized as the earliest spaceborne sensor to provide a middle infrared (MWIR) channel sensitive to actively burning vegetation fires and other high-temperature, sub-pixel thermal anomalies, forming the basis of countless fire studies before higher quality MODIS data became available. Here, we consider in the context of active fire monitoring a sensor much older than the AVHRR, namely the High Resolution Infrared Radiometer (HRIR) on board NASA’s Nimbus-I, -II, and -III satellites, launched in 1964, 1966, and 1969, respectively. This early imaging sensor had a single MWIR channel primarily intended for night-time meteorological observations (a complementary daytime near-infrared channel was added for Nimbus-III) that could potentially serve as a source of pre-AVHRR active fire data. To this end, we inspected night-time HRIR imagery acquired over the western United States and found unambiguous evidence of multiple confirmed wildfires in 1966. In addition, we found strong evidence of actively burning fires in Africa, specifically the Democratic Republic of the Congo, western Zambia, and northern Namibia, and in northern Australia, commensurate with their fire seasons. Our findings indicate that it was actually the HRIR that acquired the earliest MWIR imagery that could be used for identifying biomass burning from space, well over a decade before the first AVHRR was deployed. We found in addition that the retrieval of fire radiative power (FRP) from higher dynamic-range HRIR observations was potentially possible pending refined calibration.

Convolutional neural network advances in demosaicing for fluorescent cancer imaging with color-near-infrared sensors.

Single-chip imaging devices featuring vertically stacked photodiodes and pixelated spectral filters are advancing multi-dye imaging methods for cancer surgeries, though this innovation comes with a compromise in spatial resolution. To mitigate this drawback, we developed a deep convolutional neural network (CNN) aimed at demosaicing the color and near-infrared (NIR) channels, with its performance validated on both pre-clinical and clinical datasets. We introduce an optimized deep CNN designed for demosaicing both color and NIR images obtained using a hexachromatic imaging sensor. A residual CNN was fine-tuned and trained on a dataset of color images and subsequently assessed on a series of dual-channel, color, and NIR images to demonstrate its enhanced performance compared with traditional bilinear interpolation. Our optimized CNN for demosaicing color and NIR images achieves a reduction in the mean square error by 37% for color and 40% for NIR, respectively, and enhances the structural dissimilarity index by 37% across both imaging modalities in pre-clinical data. In clinical datasets, the network improves the mean square error by 35% in color images and 42% in NIR images while enhancing the structural dissimilarity index by 39% in both imaging modalities. We showcase enhancements in image resolution for both color and NIR modalities through the use of an optimized CNN tailored for a hexachromatic image sensor. With the ongoing advancements in graphics card computational power, our approach delivers significant improvements in resolution that are feasible for real-time execution in surgical environments.

A novel lysosome-targeting BODIPY-based fluorescent probe with two near-infrared channel signals for ratiometric detection of HClO and its application in diabetes mice model

Diabetes is an endocrine and metabolic disease characterized by hyperglycemia, which can lead to a variety of serious complications. Hypochlorous acid (HClO), one of the significant members of reactive oxygen species (ROS), is essential for various physiological processes, and the acidic lysosomes of phagocytes are one of its main sources. The related studies suggest that abnormal levels of HClO in the body have a strong association with diabetic process, but the pathological mechanisms of HClO on diabetic diseases are still unclear. Here, a novel ratiometric lysosome-targeted BODIPY-based fluorescence probe Lyso-HClO was designed for specific recognition of HClO. Lyso-HClO could rapidly sense HClO within 20 s, and its limit of detection was calculated as low as 45 nM. Both the emission wavelengths of Lyso-HClO before and after reaction of HClO reached the near-infrared region, which effectively reduced tissue damage and eliminated background interference from organisms to achieve more accurate analysis. Lyso-HClO was successfully employed for fluorescence imaging of both exogenous and endogenous HClO in RAW264.7 cells, HepG2 cells, and zebrafish. Additionally, it was effectively utilized to monitor the HClO fluctuations in diabetic mice, exhibiting a 15-fold increase in the fluorescence signal ratio (I650/I685). Therefore, the probe Lyso-HClO has enormous potential for the early diagnosis and prevention of diabetes and its complications.

LIME: Lunar Irradiance Model of ESA, a new tool for absolute radiometric calibration using the Moon

Abstract. Absolute calibration of Earth observation (EO) sensors is key to ensuring long-term stability and interoperability, and it is essential for long-term global climate records and forecasts. The Moon provides a photometrically stable calibration source within the range of the Earth's radiometric levels and is free from atmospheric interference. However, to use this ideal calibration source, one must model the variation in its disc-integrated irradiance resulting from changes in Sun–Earth–Moon geometries. The Lunar Irradiance Model of the European Space Agency (LIME) is a new lunar irradiance model developed from ground-based observations acquired using a lunar radiometer operating from the Izaña Atmospheric Observatory near Mount Teide, located in Tenerife, Spain. Nightly top-of-atmosphere (TOA) irradiance is determined using the Langley plot method, and each observation is traceable to the international system of units (SI) through the radiometer calibration performed at the National Physical Laboratory (NPL). Approximately 590 lunar observations acquired between March 2018 and December 2022 currently contribute to the model parameter derivation, which builds on the widely used ROLO (Robotic Lunar Observatory) model analytical formulation. This paper presents the strategy used to derive LIME parameters: the characterisation of the lunar radiometer, the derivation of nightly top-of-atmosphere lunar irradiance and a description of the model parameter derivation, along with the associated metrologically rigorous uncertainty. The model output has been compared to PROBA-V, Pléiades and Sentinel-3B, as well as to the VITO implementation of the ROLO model. Initial results indicate that LIME predicts 3 %–5 % higher lunar-disc-integrated irradiance than the ROLO model for the visible and near-infrared channels. The model output has an expanded (k=2) radiometric uncertainty of ∼ 2 % at the lunar radiometer wavelengths, and it is expected that planned observations until at least 2024 further constrain the model parameters in subsequent updates.

Integrating Sigmoid Calibration Function into Entropy Thresholding Segmentation for Enhanced Recognition of Potholes Imaged Using a UAV Multispectral Sensor

This study was aimed at enhancing pothole detection by combining sigmoid calibration function and entropy thresholding segmentation on UAV multispectral imagery. UAV imagery was acquired via the flying of the DJI Matrice 600 (M600) UAV system, with the MicaSense RedEdge imaging sensor mounted on its fixed wing. An endmember spectral pixel denoting pothole feature was selected and used as the base from which spectral radiance patterns of a pothole were analyzed. A field survey was carried out to measure pothole diameters, which were used as the base on which the pothole area was determined. Entropy thresholding segmentation was employed to classify potholes. The sigmoid calibration function was used to reconfigure spectral radiance properties of the UAV spectral bands to pothole features. The descriptive statistics was computed to determine radiance threshold values to be used in demarcating potholes from the reconfigured or calibrated spectral bands. The performance of the sigmoid calibration function was evaluated by analyzing the area under curve (AUC) results generated using the Relative Operating Characteristic (ROC) technique. Spectral radiance pattern analysis of the pothole surface revealed high radiance values in the red channel and low radiance values in the near-infrared (NIR) channels of the spectrum. The sigmoid calibration function radiometrically reconfigured UAV spectral bands based on a total of 500 sampled pixels of pothole surface obtained from all the spectral channels. Upon successful calibration of UAV radiometric properties to pothole surface, the reconfigured mean radiance values for pothole surface were noted to be 0.868, 0.886, 0.944, 0.211 and 0.863 for blue, green, red, NIR and red edge, respectively. The area under curve (AUC) results revealed the r2 values of 0.53, 0.35, 0.71, 0.19 and 0.35 for blue, green, red, NIR and red edge spectral channels, respectively. Overestimation of pothole 1 by both original and calibrated spectral channels was noted and can be attributed to the presence of soils adjacent to the pothole. However, calibrated red channel estimated pothole 2 and pothole 3 accurately, with a slight area deviation from the measured potholes. The results of this study emphasize the significance of reconfiguring radiometric properties of the UAV imagery for improved recognition of potholes.

Climatology of gravity wave activity based on two Martian years from ACS/TGO observations

Context. Gravity waves redistribute energy and momentum between the lower and upper atmosphere, thus providing vertical coupling between layers, and they affect the state, dynamics, and variability of the upper atmosphere. The statistics of gravity wave activity on Mars is poorly explored but is required in order to characterize the atmospheric circulation and to constrain numerical models. Aims. We present the gravity wave statistics accumulated over two Martian years: from the second half of Martian year 34 to the middle of Martian year 36 (May 2018 to February 2022). The statistics includes seasonal and latitude distributions of the wave potential energy and drag, serving to represent the wave activity and impact on the atmospheric dynamics. Methods. The observations were performed by the middle- and near-infrared spectrometers of the Atmospheric Chemistry Suite on board the ExoMars Trace Gas Orbiter. The temperature profiles we obtained independently from both channels during simultaneous measurements show a good agreement, thus providing verification and enhancing confidence in the data. The gravity wave parameters included amplitudes of temperature fluctuations, potential energy per unit mass, and wave drag. These parameters were retrieved at altitudes up to 160 and 100 km from the middle- and near-infrared channels, respectively. Results. A comparison of the data obtained during the global dust storm of Martian year 34 with the corresponding period of Martian year 35 without a storm revealed a reduction of wave activity in mid-latitudes, which is in agreement with previous observations, and enhancement in the polar regions of the southern hemisphere, which was not predicted by simulations with a high-resolution circulation model.

Comprehensive Evaluation of Multispectral Image Registration Strategies in Heterogenous Agriculture Environment.

This article is focused on the comprehensive evaluation of alleyways to scale-invariant feature transform (SIFT) and random sample consensus (RANSAC) based multispectral (MS) image registration. In this paper, the idea is to extensively evaluate three such SIFT- and RANSAC-based registration approaches over a heterogenous mix containing Triticum aestivum crop and Raphanus raphanistrum weed. The first method is based on the application of a homography matrix, derived during the registration of MS images on spatial coordinates of individual annotations to achieve spatial realignment. The second method is based on the registration of binary masks derived from the ground truth of individual spectral channels. The third method is based on the registration of only the masked pixels of interest across the respective spectral channels. It was found that the MS image registration technique based on the registration of binary masks derived from the manually segmented images exhibited the highest accuracy, followed by the technique involving registration of masked pixels, and lastly, registration based on the spatial realignment of annotations. Among automatically segmented images, the technique based on the registration of automatically predicted mask instances exhibited higher accuracy than the technique based on the registration of masked pixels. In the ground truth images, the annotations performed through the near-infrared channel were found to have a higher accuracy, followed by green, blue, and red spectral channels. Among the automatically segmented images, the accuracy of the blue channel was observed to exhibit a higher accuracy, followed by the green, near-infrared, and red channels. At the individual instance level, the registration based on binary masks depicted the highest accuracy in the green channel, followed by the method based on the registration of masked pixels in the red channel, and lastly, the method based on the spatial realignment of annotations in the green channel. The instance detection of wild radish with YOLOv8l-seg was observed at a mAP@0.5 of 92.11% and a segmentation accuracy of 98% towards segmenting its binary mask instances.

Fusarium head blight detection, spikelet estimation, and severity assessment in wheat using 3D convolutional neural networks

Fusarium head blight (FHB) is one of the most significant diseases affecting wheat and other small grain cereals worldwide. Developing FHB-resistant cultivars is critical but requires field and greenhouse disease assessment which are typically laborious and time-consuming. In this work, we developed automated applications based on 3-dimensional (3D) convolutional neural networks (CNN) that detect FHB symptoms expressed in wheat, estimate the total number of spikelets versus the total number of infected spikelets on a wheat head, and subsequently calculate FHB severity index. Such tools are an important step toward the creation of automated and efficient phenotyping methods. The data used to generate the results are 3D point clouds consisting of four color channels – red, green, blue (RGB), and near-infrared (NIR) – collected using a multispectral 3D scanner. Our 3D CNN models for FHB detection achieved 100% accuracy. The influence of the multispectral information on performance was evaluated; the results showed the dominance of the RGB channels over both the NIR (720 nm peak wavelength) and the NIR plus RGB channels combined. Our best 3D CNN models for estimation of total and infected number of spikelets achieved mean absolute errors (MAE) of 1.13 and 1.56, respectively. Our best 3D CNN models for FHB severity estimation achieved 8.6 MAE. A linear regression analysis between the visual FHB severity assessment and the FHB severity predicted by our 3D CNN showed a significant correlation.

High-accuracy FY-3A/MERSI precipitable water vapour (PWV) data over the continental United States

ABSTRACT Water vapour is an important atmospheric gas with a direct impact on climate and weather. Given the significant variations of water vapour distribution in the tridimensional and temporal space, it is of great significance to use remote sensing data to monitor it on a large scale. One of the main missions of the Medium Resolution Spectral Imager (MERSI) onboard FengYun (FY)-3A satellite is to monitor the distribution of precipitable water vapour (PWV). However, the official MERSI PWV data is not widely used due to the limitation of its accuracy. The primary objective of this study is to develop FY-3A/MERSI PWV data with higher accuracy. The study area of this paper is the continental United States (125°W–65°W, 25°–50°N), and the selected period is the year 2010. Three PWV retrieval models for three near-infrared (NIR) channels (0.905, 0.940 and 0.980 μm) of MERSI are constructed based on the matching results between MERSI data and SuomiNET PWV data. Compared with the existing semi-empirical PWV retrieval algorithms, the algorithm developed in this work has two main changes: (1) MERSI PWV retrieval models are constructed using data after removing outliers rather than all data. (2) The algorithm can identify abnormal retrieval results. The validation results based on PWV data derived from Aerosol Robotic NETwork (AERONET) indicate that these two changes both contribute positively to enhancing the accuracy of MERSI PWV data. The root mean square errors (RMSE) of MERSI PWV data developed using the three channels at 0.905, 0.940 and 0.980 μm are 0.28 cm, 0.22 cm and 0.24 cm, respectively. The relative errors (RE) of these PWV data are 0.14, 0.10 and 0.12, respectively. In comparison to the official MERSI PWV data, the RMSE of MERSI PWV data retrieved in this study is reduced by at least 44%, and RE is reduced by at least 42%.

A dual-channel visible light optical coherence tomography system enables wide-field, full-range, and shot-noise limited human retinal imaging

Visible light optical coherence tomography (VIS-OCT) is an emerging ophthalmic imaging method featuring ultrahigh depth resolution, retinal microvascular oximetry, and distinct scattering contrast in the visible spectral range. The clinical utility of VIS-OCT is hampered by the fundamental trade-off between the imaging depth range and axial resolution, which are determined by the spectral resolution and bandwidth, respectively. To address this trade-off, here we developed a dual-channel VIS-OCT system with three major advancements including the first linear-in-K VIS-OCT spectrometer to decrease the roll-off, reference pathlength modulation to expand the imaging depth range, and per-A-line noise cancellation to remove excess noise, Due to these unique designs, this system achieves 7.2 dB roll-off over the full 1.74 mm depth range (water) with shot-noise limited performance. The system uniquely enables >60° wide-field imaging which would allow simultaneous imaging of the peripheral retina and optic nerve head, as well as ultrahigh 1.3 µm depth resolution (water). Benefiting from the additional near-infrared (NIR) channel of the dual-channel design, this system is compatible with Doppler OCT and OCT angiography (OCTA). The comprehensive structure-function measurement enabled by this dual-channel VIS-OCT system is an advance towards adoption of VIS-OCT in clinical applications.

High Speed Dual‐Band Photodetector for Dual‐Channel Optical Communications in Wavelength Division Multiplexing and Security Enhancement

AbstractThe forthcoming wireless networks must integrate to a high density of end‐user devices while ensuring a superior quality of service. It is imperative to devise solutions that reduce the density of wireless access points and associated costs, while simultaneously ensuring security and privacy throughout propagation. Herein, a novel dual‐band photodetector with high selective responsivities in distinct visible and near‐infrared (NIR) band is developed as an optical signal receiver. The receiver has an unprecedented fast switch speed > 500 kHz between different operation modes. Leveraging this fast switch speed, a high‐speed Multiple‐Input‐Single‐Output (MISO) system is developed, fusing spectrum resources from visible to infrared bands. The system achieves a data rate of 600 Kbps in the visible channel and 500 Kbps in the NIR channel, with a sum rate of 1.1 Mbps. This is the highest single‐channel data rate and sum rate among reported dual‐band photodetectors. Additionally, two previously overlooked mechanisms, the Subtraction and Addition modes, are studied in detail. The four operation modes are developed to establish a novel physical encryption method in terms of arithmetic relation. The dual‐channel coupled link can support a data rate of 200 Kbps, significantly enhancing communication security.

Precipitable Water Vapor Retrieval Based on Near-Infrared Channel Data from FengYun-3G Satellite/Medium Resolution Spectral Imager-Rainfall Mission

Precipitable Water Vapor Retrieval Based on Near-Infrared Channel Data from FengYun-3G Satellite/Medium Resolution Spectral Imager-Rainfall Mission

A large population of strongly lensed faint submillimetre galaxies in future dark energy surveys inferred from JWST imaging

ABSTRACT Bright galaxies at submillimetre wavelengths from Herschel are now well known to be predominantly strongly gravitationally lensed. The same models that successfully predicted this strongly lensed population also predict about 1 per cent of faint 450 μm-selected galaxies from deep James Clerk Maxwell Telescope (JCMT) surveys will also be strongly lensed. Follow-up ALMA campaigns have so far found one potential lens candidate, but without clear compelling evidence, for example, from lensing arcs. Here, we report the discovery of a compelling gravitational lens system confirming the lensing population predictions, with a zs = 3.4 ± 0.4 submm source lensed by a zspec = 0.360 foreground galaxy within the COSMOS field, identified through public JWST imaging of a 450 μm source in the SCUBA-2 Ultra Deep Imaging EAO Survey (STUDIES) catalogue. These systems will typically be well within the detectable range of future wide-field surveys such as Euclid and Roman, and since submillimetre galaxies are predominantly very red at optical/near-infrared wavelengths, they will tend to appear in near-infrared channels only. Extrapolating to the Euclid-Wide survey, we predict tens of thousands of strongly lensed near-infrared galaxies. This will be transformative for the study of dusty star-forming galaxies at cosmic noon, but will be a contaminant population in searches for strongly lensed ultra-high-redshift galaxies in Euclid and Roman.

A neural-network-based method for generating synthetic 1.6 µm near-infrared satellite images

Abstract. In combination with observations from visible satellite channels, near-infrared channels can provide valuable additional cloud information, e.g. on cloud phase and particle sizes, which is also complementary to the information content of thermal infrared channels. Exploiting near-infrared channels for operational data assimilation and model evaluation requires a sufficiently fast and accurate forward operator. This study presents an extension to the method for fast satellite image synthesis (MFASIS) that allows for simulating reflectances of the 1.6 µm near-infrared channel based on a computationally efficient neural network with the same accuracy that has already been achieved for visible channels. For this purpose, it is important to better represent vertical variations in effective cloud particle radii, as well as mixed-phase clouds and molecular absorption in the idealized profiles used to train the neural network. A new approach employing a two-layer model of water, ice and mixed-phase clouds is described, and the relative importance of the different input parameters characterizing the idealized profiles is analysed. A comprehensive data set sampled from Integrated Forecasting System (IFS) forecasts together with different parameterizations of the effective water and ice particle radii is used for the development and evaluation of the method. Further evaluation uses a month of ICOsahedral Non-hydrostatic development based on version 2.6.1 (ICON-D2) hindcasts with effective radii directly determined by the two-moment microphysics scheme of the model. In all cases, the mean absolute reflectance error achieved is about 0.01 or smaller, which is an order of magnitude smaller than typical differences between reflectance observations and corresponding model values. The errors related to the imperfect training of the neural networks present only a small contribution to the total error, and evaluating the networks takes less than a microsecond per column on standard CPUs. The method is also applicable for many other visible and near-infrared channels with weak water vapour sensitivity.

Near-infrared fluorescence tattooing: a new approach for endoscopic marking of tumors in minimally invasive colorectal surgery using a persistent near-infrared marker

IntroductionIntraoperative accurate localization of tumors in the lower gastrointestinal tract is essential to ensure oncologic radicality. In minimally invasive colon surgery, tactile identification of tumors is challenging due to diminished or absent haptics. In clinical practice, preoperative endoscopic application of a blue dye (ink) to the tumor site has become the standard for marking and identification of tumors in the colon. However, this method has the major limitation that accidental intraperitoneal spillage of the dye can significantly complicate the identification of anatomical structures and surgical planes. In this work, we describe a new approach of NIR fluorescent tattooing using a near-infrared (NIR) fluorescent marker instead of a blue dye (ink) for endoscopic tattooing.MethodsAFS81x is a newly developed NIR fluorescent marker. In an experimental study with four domestic pigs, the newly developed NIR fluorescent marker (AFS81x) was used for endoscopic tattooing of the colon. 7–12 endoscopic submucosal injections of AFS81x were placed per animal in the colon. On day 0, day 1, and day 10 after endoscopic tattooing with AFS81x, the visualization of the fluorescent markings in the colon was evaluated during laparoscopic surgery by two surgeons and photographically documented.ResultsThe detection rate of the NIR fluorescent tattoos at day 0, day 1, and day 10 after endoscopic tattooing was 100%. Recognizability of anatomical structures during laparoscopy was not affected in any of the markings, as the markings were not visible in the white light channel of the laparoscope, but only in the NIR channel or in the overlay of the white light and the NIR channel of the laparoscope. The brightness, the sharpness, and size of the endoscopic tattoos did not change significantly on day 1 and day 10, but remained almost identical compared to day 0.ConclusionThe new approach of endoscopic NIR fluorescence tattooing using the newly developed NIR fluorescence marker AFS81x enables stable marking of colonic sites over a long period of at least 10 days without compromising the recognizability of anatomical structures and surgical planes in any way.

Dual-channel fluorescent probe for simultaneously detecting H2S and viscosity/polarity and its application in non-alcoholic fatty liver, tumor tissue, and food spoilage

Intracellular hydrogen sulfide (H2S) and micro-environments (e.g. viscosity and polarity) are tightly correlated with various physiological/pathological processes, making them potential biomarkers of many diseases. However, the simultaneously detecting H2S, viscosity, and polarity in inflammation, non-alcoholic fatty liver (NAFL) and tumor tissues containing clinical cancer patient samples has not been achieved yet due to the lack of effective tools. Herein, we presented a mitochondria-targeting near-infrared (NIR) fluorescent probe MQA-DNP) for simultaneously monitoring H2S, viscosity/polarity through dual channels. The probe could specifically recognize H2S via far-red emission (λem = 634 nm), and exhibited high sensitivity toward micro-environmental viscosity/polarity in the NIR channel (λem > 714 nm). Facilitated with the probe, we revealed for the first time that inflammation cells and NAFL tissues are accompanied by an up-regulation of H2S, as well as an increase in viscosity level (and/or decrease in polarity degree). Surprisingly, the simultaneous visualization of H2S, viscosity/polarity has also been achieved not only at the cancer cells and tumor models, but also in clinical cancer patients. Compared to detecting a single biomarker, the simultaneous tracking multi-markers through multi-channels seems to be more reliable for visual medical diagnosis of mitochondria-related diseases. Furthermore, inspired by the significant color changes and high sensitivity of MQA-DNP reaction with H2S, MQA-DNP has been successfully used to detect H2S released during food spoilage through test strips, and has great potential for application in complex environments systems.

Ratiometric fluorescent probe with signals from two near-infrared channels for esterase activity and its application in biological imaging

Esterase serves as a crucial biomarker for the diagnosis of tumors due to its overexpression in cancer cells. Ratiometric fluorescent probe has the advantage of having two distinct emission wavelengths, which allows for the elimination of the effects of light intensity and optical path length by using the fluorescence intensity ratio between the two emission wavelengths. In this work, a novel near-infrared (NIR) fluorescent probe P1 for ratiometric monitor esterase activity was developed. Compared with ordinary ratiometric probes, both emission wavelengths of this probe are in the NIR region, which can effectively eliminate various fluctuations and blurriness in image contrast and achieve precise quantitative analysis. P1 itself shows a fluorescence emission at 717 nm. After incubated with esterase, a new emission peak at 822 nm emerged due to esterase's catalysis, and the fluorescence intensity at 717 nm steadily decreased, which achieved the highly sensitive detection of esterase with a detection limit calculated as 0.25 mU/mL. What’s more, P1 showed high selectivity for esterase and was successfully applied for biological imaging in living cells and in vivo.

Predicting Nitrogen Content in Oil Palms through Machine Learning and RGB Aerial Imagery

—Nitrogen is a crucial nutrient for the sustainable health and productivity of oil palm plantations. Accurate fertilization for Nitrogencan optimize production while reducing maintenance costs. This study investigates the relationship between various vegetation indices and oil palm Nitrogen content using aerial images. We employ and compare different machine learning algorithms to predict Nitrogen content in oil palms, utilizing RGB aerial images obtained from PT. Perkebunan Nusantara IV (PTPN IV) in North Sumatra. Twelve vegetation indices are assessed, considering the limited spectral information available from the aerial images. Our findings reveal that the random forest algorithm, when applied to Hue, Green Leaf Index, and Coloration Index, yields the highest prediction accuracy of 90.13%. Furthermore, the results demonstrate that machine learning algorithms can effectively overcome the limitations of near-infrared channel availability, allowing for the prediction of Nitrogen content using RGB aerial images as a proxy for chlorophyll absorption.