High-Throughput All-Optical Determination of Nanorod Size and Orientation.

As a single-particle characterization technique, optical microscopy has transformed our understanding of structure-function relationships of plasmonic nanoparticles, but the need for ex-situ-correlated electron microscopy to obtain structural information handicaps an otherwise exceptional high-throughput technique. Here, we present an all-optical alternative to electron microscopy to accurately and quickly extract structural information about single gold nanorods (Au NRs) using calcite-assisted localization and kinetics (CLocK) microscopy. Color CLocK images of single Au NRs allow scattering from the longitudinal and transverse plasmon modes to be imaged simultaneously, encoding spectral data in CLocK images that can then be extracted to obtain Au NR size and orientation. Moreover, through the use of convolutional neural networks, Au NR length, width, and aspect ratio can be predicted directly from color CLocK images within ∼10% of the true value measured by electron microscopy.

Presentation of microstructural diffusion components by color schemes in abdominal organs.

PURPOSE: Development of a color scheme representation to facilitate the interpretation of tri-exponential DWI data from abdominal organs, where multi-exponential behavior is more pronounced. METHODS: Multi-exponential analysis of DWI data provides information about the microstructure of the tissue under study. The tri-exponential signal analysis generates numerous parameter images that are difficult to analyze individually. Summarized color images can simplify at-a-glance analysis. A color scheme was developed in which the slow, intermediate, and fast diffusion components were each assigned to a different red, green, and blue color channel. To improve the appearance of the image, histogram equalization, gamma correction, and white balance were used, and the processing parameters were adjusted. Examples of the resulting color maps of the diffusion fractions of healthy and pathological kidney and prostate are shown. RESULTS: The color maps obtained by the presented method show the merged information of the slow, intermediate, and fast diffusion components in a single view. A differentiation of the different fractions becomes clearly visible. Fast diffusion regimes, such as in the renal hilus, can be clearly distinguished from slow fractions, such as in dense tumor tissue. CONCLUSION: Combining the diffusion information from tri-exponential DWI analysis into a single color image allows for simplified interpretation of the diffusion fractions. In the future, such color images may provide additional information about the microstructural nature of the tissue under study.

Synthesis of a Multi-Stimulus Responsive RGB Fluorescent Organic Molecule based on Dark Through-Bond Energy Transfer Mechanism.

In this study, a novel multi-stimulus responsive RGB fluorescent organic molecule, RTPE-NH2, was designed and synthesized based on the combination of aggregation-induced emission tetraphenylethylene (TPE) luminophore and acid-responsive fluorescent molecular switch Rhodamine B. RTPE-NH2 exhibits aggregation-induced emission behavior, as well as UV irradiation-stimulus and acid-stimulus responsive fluorescence properties. It could emit orange-red (R), green(G), and blue(B) light in both solution and PMMA film under 365 nm excitation. The dark through-bond energy transfer (DTBET) mechanism was proposed and supported by control experiments and TD-DFT calculations. The synthesis and application of RTPE-NH2 could accelerate the development of organic smart materials with high sensitivity and excellent optical properties.

Smartphone-Based Quantitative Measurement of Cu2+: Fluorescent Turn-on Chemosensor via Radical Cation Formation.

We report a unique radical cation formation-based fluorescent chemosensor (E)-N'-(4-(diphenylamino)benzylidene)thiophene-2-carbohydrazide (DBTC) that quantitatively determines Cu2+ based on the RGB model using a smartphone. DBTC exhibited a weak turquoise fluorescence due to fluorescence suppression by amide isomerization. When Cu2+ was added into DBTC, it showed strong light blue fluorescence with a high quantum yield ([Formula: see text] = 0.470). The detection limit of Cu2+ was determined to be 0.40 µM at the concentration range of 0-7.5 µM. In addition, the detection mechanism of DBTC for Cu2+ was demonstrated to be an oxidative cyclization reaction through 1H NMR titration, ESI-MS analysis, and DFT calculation. Remarkably, DBTC could be applied to the quantitative measurement of Cu2+ using a smartphone and RGB analysis. The detection limit was calculated to be 0.05 µM, which is the lowest detection limit among chemosensors that could detect Cu2+ through smartphone-based fluorescence measurements. Additionally, spike and recovery experiments conducted with different concentrations of Cu2+ showed good recovery values. DBTC exhibited its potential as a chemosensor for determining Cu2+ through the application of a smartphone-based platform capable of real-time monitoring.

Colorimetric enzymatic rapid test for the determination of atropine in baby food using a smartphone.

A method for the enzymatic determination of atropine has been developed, which is based on a sequence of reactions involving (1) the hydrolysis of atropine to give tropine; (2) the enzymatic oxidation of tropine with NAD (catalysed by tropinone reductase); and (3) an indicator reaction, in which the NADH previously formed reduces the dye iodonitrotetrazolium chloride (INT) to a reddish species, the reaction catalysed by diaphorase. The method was first developed in solution (linear response range from 2.4 × 10-6 M to 1.0 × 10-4 M). It was then implemented in cellulose platforms to develop a rapid test where the determination is made by measuring the RGB coordinates of the platforms using a smartphone-based device. The device is based on the integrating sphere concept and contains a light source to avoid external illumination effects. The smartphone is controlled by an app that allows a calibration line to be generated and the atropine concentration to be quantified; moreover, since the app normalizes the CCD response of the smartphone, the results and calibrations obtained with different smartphones are similar and can be shared. Using the G coordinate, the results were shown to have a linear response with the concentration of atropine ranging from 1.2 × 10-5 M to 3.0 × 10-4 M with an RSD of 1.4% (n = 5). The method has been applied to the determination of atropine in baby food and buckwheat samples with good results.

Portable smartphone-assisted highly sensitive detection of mercury ions based on gold nanoparticle-modified NH2-UiO-66 metal-organic framework.

A novel portable smartphone-assisted colorimetric method was reported for the determination of Hg2+ with good analytical performance. A Zr(IV)-based metal-organic framework functionalized with amino groups (NH2-UiO-66) has been adopted as a supporting platform to anchor gold nanoparticles (AuNPs), avoiding the migration and aggregation of AuNPs. With the addition of Hg2+, the formation of gold amalgam proved possible to enhance peroxidase-like activity of the composite (AuNPs/NH2-UiO-66), accelerating the oxidization of zymolyte 3,3',5,5'-tetramethylbenzidine (TMB). In the meantime, the color of the reaction solution turned a vivid blue, and the red, green, and blue (RGB) values of the solution color changed accordingly. On account of this strategy, the quantitative detection of Hg2+ could be achieved. After the optimization of the experiment conditions, the average color intensity (Ic) resulting from RGB values was linear related to the concentration of Hg2+ from 10 to 100 nM, accompanied with a detection limit (LOD) down to 5.4 nM calculated by 3σ/S. The successful application of the designed method has been promoted to detect Hg2+ in some water samples, displaying a great potential in practical application. Furthermore, the use of a smartphone made our proposed method simple and accurate, and thus puts forward a possible way for in situ and real-time monitoring.

Protocol for metadata and image collection at diabetic foot ulcer clinics: enabling research in wound analytics and deep learning.

BACKGROUND: The escalating impact of diabetes and its complications, including diabetic foot ulcers (DFUs), presents global challenges in quality of life, economics, and resources, affecting around half a billion people. DFU healing is hindered by hyperglycemia-related issues and diverse diabetes-related physiological changes, necessitating ongoing personalized care. Artificial intelligence and clinical research strive to address these challenges by facilitating early detection and efficient treatments despite resource constraints. This study establishes a standardized framework for DFU data collection, introducing a dedicated case report form, a comprehensive dataset named Zivot with patient population clinical feature breakdowns and a baseline for DFU detection using this dataset and a UNet architecture. RESULTS: Following this protocol, we created the Zivot dataset consisting of 269 patients with active DFUs, and about 3700 RGB images and corresponding thermal and depth maps for the DFUs. The effectiveness of collecting a consistent and clean dataset was demonstrated using a bounding box prediction deep learning network that was constructed with EfficientNet as the feature extractor and UNet architecture. The network was trained on the Zivot dataset, and the evaluation metrics showed promising values of 0.79 and 0.86 for F1-score and mAP segmentation metrics. CONCLUSIONS: This work and the Zivot database offer a foundation for further exploration of holistic and multimodal approaches to DFU research.

Quinoline-quinoline schiff-base as an effective chromogenic, fluorogenic, and smartphone assisted RGB detection of Pb2+ ion in near aqueous medium.

A novel multimode colorimetric and fluorescent chemosensor was developed using an 8-hydroxy quinoline carbaldehyde Schiff base with a quinoline hydrazide probe (E)-2-((2-(quinolin-2-yl)hydrazineylidene)methyl)quinolin-8-ol (L). NMR (1H & 13C), FTIR, and HR-mass spectral characterization techniques confirmed the probe L structural conformation. As Probe L contacts Pb2+ ions, a color change and turn-off emission can be visually detected in EtOH:H2O (1:1, v/v, pH = 7.21) medium. The probe displays a good emission at 440 nm due to the combined ESIPT and ICT process. The Pb2+ ion interacts with the probe and selectively quenches fluorescence by inhibiting ESIPT and >CN- isomerization. As per Job's plot, L-Pb2+ complex formation occurred in a 1:1 stoichiometric ratio, with association constant (Ka) and quenching constant (Ksv) estimated at 1.52 × 105 M-1 and 4.12 × 105 M, respectively. The detection limits of Pb2+ by spectrophotometric and spectrofluorometric were 1.99 µM (41 ppb) and 23.4 nM (485 ppt), respectively. Additionally, the test paper kit and RGB tool were used to monitor the color changes of L with Pb2+ and the LOD was found to be 5.99 µM (125 ppb). Its recognition mechanism has been verified by 1H NMR, ESI-mass, and theoretical studies.

Rapid assessment of soil accessible Cr(â ¥) in the field by a portable RGB color sensor.

Field rapid determination of soil accessible Cr(Ⅵ) is of great significance for on-site assessment and decision-making about the health risks of contaminated sites. When the thickness of solutions with various concentrations of Cr(Ⅵ) is constant, there would be a quantitative relationship between the chromogenic difference of Cr(Ⅵ) solutions and the concentration of Cr(Ⅵ). The chromogenic difference could be described by Red (R), Green (G), Blue (B) values. Based on the chromogenic reaction between 1,5-diphenylcarbazide and Cr(Ⅵ), this study first established the calibration curve between the chromogenic difference and the concentration of Cr(Ⅵ) in standard solution with or without 0.01 M CaCl2, using an RGB color sensor. This is the subsequent determination basis of the method for rapidly assessing accessible Cr(Ⅵ) in the field (M-RGB). Then, the concentration of accessible Cr(Ⅵ) of contaminated soil with "hand-shaking + standing" field extraction method was compared with "end-over-end shaking" laboratory extraction method. Finally, the accessible Cr(Ⅵ) of contaminated soil extractants was determined via M-RGB integrating the field extraction method. Results indicated there was a highly significant linear relationship between colorimetric difference value (∆E) and Cr(Ⅵ) concentration in the range of 0.1-3 mg/L (R2 > 0.99, P < 0.01), based on the Euclidean formula for calculating ∆E. The "hand-shaking + standing" field extraction method was effective in obtaining accessible Cr(Ⅵ) extractants with or without 0.01 M CaCl2, with the high extraction efficiency within 100±1%. The concentrations of accessible Cr(Ⅵ) in various polluted soils determined by M-RGB were consistent with that determined by the ultraviolet-visible spectrophotometry, with the relative error within ±5%, and the relative standard deviation ≤ 20%. The spiked recovery experiments showed that the recovery of M-RGB was between 95% and 105%, which means M-RGB could realize the trace analysis for accessible Cr(Ⅵ) in the field.

A smartphone-assisted portable dual-mode immunoassay of gentamicin based on curcumin nanoparticles and carbon dots.

A smartphone-assisted portable dual-mode immunoassay was constructed based on curcumin nanoparticles (CNPs) and carbon dots (CDs) for gentamicin (GEN) detection. CNPs were labeled with goat anti-mouse IgG (Ab2) to create a conjugation that coupled dual signals to concentrations of GEN antigens. CNPs were introduced to pH 7.4 water and showed insignificant color and optical responses. When exposed to the high pH environment, the structure of CNPs changed and color and optical properties were restored. Because of the inner filter effect (IFE) between CNPs and CDs, the fluorescence of CNPs at 550 nm quenched the fluorescence of CDs at 450 nm. Colorimetry and ratiometric fluorescence (F550 nm/F450 nm) dual-mode immunoassay linearly correlated with GEN ranged from 10-4 to 100 µg/mL with a detection limit (LOD) of 8.98 × 10-5 µg/mL and 4.66 × 10-5 µg/mL, respectively. This work supplied a portable, sensitive, and specific platform to detect GEN.

Portable paper-based probe for on-site ratiometric fluorescence determination of total flavonol glycosides in plant extract using smartphone imaging.

A smartphone-assisted, paper-based ratio fluorescence probe is presented for the rapid, low-cost and on-site quantification of total flavonol glycosides in Ginkgo biloba extracts (GBE). The Al3+/Eu-MOF/paper-based probe utilizes lanthanide metal-organic framework (Ln-MOF) nanoparticles immobilized on Whatman filter paper along with Al3+ for detecting flavonols, which are the hydrolyzed products of flavonol glycosides. The color change of the paper-based fluorescence image from red to orange depends on the concentration of the target analyte in the sample solution. The smartphone equipped with a red, green, blue (RGB) color detector measured the fluorescence signal intensity on the paper substrate after adding flavonol. The analytical variables affecting the performance of the probe, including the addition sequence of the aluminum nitrate solution, its concentration, that of the Ln-MOF solution, the drying time of the paper probe, the reaction time and the sensitivity parameters of the mobile phone camera (ISO), were optimized. Under optimal conditions, the Al3+/Eu-MOF/paper-based probe has good linear response in the concentration range 7 ~ 80 µg mL- 1 and a lower detection limit of 2.07 µg mL- 1. The results obtained with the paper-based ratio fluorescence probe and smartphone combination were validated by comparing them with high-performance liquid chromatography (HPLC) measurements. This study provides a potential strategy for fabricating Al3+/Eu-MOF/paper-based probe used for total flavonol glycosides determination.

Ratiometric fluorescence probe based on carbon dots and p-phenylenediamine-derived nanoparticles for the sensitive detection of manganese ions.

A ratiometric fluorescence probe based on carbon quantum dots with 420 nm emission (bCQDs) and a p-phenylenediamine-derived fluorescence probe with 550 nm emission (yprobe) is constructed for the detection of Mn2+. The presence of Mn2+ results in the enhanced absorption band at 400 nm of yprobe, and the fluorescence of yprobe is significantly enhanced based on the chelation-enhanced fluorescence mechanism. The fluorescence of bCQDs is then quenched based on the inner filtration effect. The ratio (I550/I420) linearly increases with the increase of Mn2+ concentration within 2.00 × 10-7-1.50 × 10-6 M, and the limit of detection is 1.76 × 10-9 M. Given the fluorescence color changing from blue to yellow, the visual sensing of Mn2+ is feasible based on bCQDs/yprobe coupled with RGB value analysis. The practicability of the proposed method has been verified in tap water, lake water, and sparkling water beverage, indicating that bCQDs/yprobe has promising application in Mn2+ monitoring.

Spectral Reconstruction from RGB Imagery: A Potential Option for Infinite Spectral Data?

Spectral imaging has revolutionisedvarious fields by capturing detailed spatial and spectral information. However, its high cost and complexity limit the acquisition of a large amount of data to generalise processes and methods, thus limiting widespread adoption. To overcome this issue, a body of the literature investigates how to reconstruct spectral information from RGB images, with recent methods reaching a fairly low error of reconstruction, as demonstrated in the recent literature. This article explores the modification of information in the case of RGB-to-spectral reconstruction beyond reconstruction metrics, with a focus on assessing the accuracy of the reconstruction process and its ability to replicate full spectral information. In addition to this, we conduct a colorimetric relighting analysis based on the reconstructed spectra. We investigate the information representation by principal component analysis and demonstrate that, while the reconstruction error of the state-of-the-art reconstruction method is low, the nature of the reconstructed information is different. While it appears that the use in colour imaging comes with very good performance to handle illumination, the distribution of information difference between the measured and estimated spectra suggests that caution should be exercised before generalising the use of this approach.

Multi-View Metal Parts Pose Estimation Based on a Single Camera.

Pose estimation of metal parts plays a vital role in industrial grasping areas. It is challenging to obtain complete point clouds of metal parts because of their reflective properties. This study introduces an approach for recovering the 6D pose of CAD-known metal parts from images captured by a single RGB camera. The proposed strategy only requires RGB images without depth information. The core idea of the proposed method is to use multiple views to estimate the metal parts' pose. First, the pose of metal parts is estimated in the first view. Second, ray casting is employed to simulate additional views with the corresponding status of the metal parts, enabling the calculation of the camera's next best viewpoint. The camera, mounted on a robotic arm, is then moved to this calculated position. Third, this study integrates the known camera transformations with the poses estimated from different viewpoints to refine the final scene. The results of this work demonstrate that the proposed method effectively estimates the pose of shiny metal parts.

Nonintrusive and Effective Volume Reconstruction Model of Swimming Sturgeon Based on RGB-D Sensor.

The sturgeon is an important commercial aquaculture species in China. The measurement of sturgeon mass plays a remarkable role in aquaculture management. Furthermore, the measurement of sturgeon mass serves as a key phenotype, offering crucial information for enhancing growth traits through genetic improvement. Until now, the measurement of sturgeon mass is usually conducted by manual sampling, which is work intensive and time consuming for farmers and invasive and stressful for the fish. Therefore, a noninvasive volume reconstruction model for estimating the mass of swimming sturgeon based on RGB-D sensor was proposed in this paper. The volume of individual sturgeon was reconstructed by integrating the thickness of the upper surface of the sturgeon, where the difference in depth between the surface and the bottom was used as the thickness measurement. To verify feasibility, three experimental groups were conducted, achieving prediction accuracies of 0.897, 0.861, and 0.883, which indicated that the method can obtain the reliable, accurate mass of the sturgeon. The strategy requires no special hardware or intensive calculation, and it provides a key to uncovering noncontact, high-throughput, and highly sensitive mass evaluation of sturgeon while holding potential for evaluating the mass of other cultured fishes.

LNMVSNet: A Low-Noise Multi-View Stereo Depth Inference Method for 3D Reconstruction.

With the widespread adoption of modern RGB cameras, an abundance of RGB images is available everywhere. Therefore, multi-view stereo (MVS) 3D reconstruction has been extensively applied across various fields because of its cost-effectiveness and accessibility, which involves multi-view depth estimation and stereo matching algorithms. However, MVS tasks face noise challenges because of natural multiplicative noise and negative gain in algorithms, which reduce the quality and accuracy of the generated models and depth maps. Traditional MVS methods often struggle with noise, relying on assumptions that do not always hold true under real-world conditions, while deep learning-based MVS approaches tend to suffer from high noise sensitivity. To overcome these challenges, we introduce LNMVSNet, a deep learning network designed to enhance local feature attention and fuse features across different scales, aiming for low-noise, high-precision MVS 3D reconstruction. Through extensive evaluation of multiple benchmark datasets, LNMVSNet has demonstrated its superior performance, showcasing its ability to improve reconstruction accuracy and completeness, especially in the recovery of fine details and clear feature delineation. This advancement brings hope for the widespread application of MVS, ranging from precise industrial part inspection to the creation of immersive virtual environments.

Design Aspects for Portable LED-Based Colorimetric Characterisation Systems Targeting Liquid Analytes.

Colorimetric characterisation systems based on LEDs and RBG sensors are straightforward to implement, are highly integrable allowing for portable measurement systems and can be constructed using widespread and affordable components. They have already proved to be a satisfactory solution in several applications related to chemical analysis. In this paper, we present an RGB sensor-based prototype for colorimetric characterisation, which can accommodate cuvettes with optical paths of 10 mm and 40 mm. We assessed the impact of experimental condition parameters such as the variability of the analyte volume in the cuvette, as well as the presence of floating particles or deposits at the bottom of the cuvette. While these would not impact the result given by a spectrophotometer that generally has a directional light source, they must be considered in LED/RGB sensor analysers in which the light path is not tightly controlled. We demonstrated that there is a minimal sensor height above the bottom of the cuvette and a minimal analyte level (both depending on the prototype optical path length) above which the analyte volume and the presence of floating particles and deposits have no impact on the prototype output signal. Finally, based on these results, we proposed a test method for a quick dye-displacement assay, in which the reagent is a dye-loaded molecularly imprinted polymer that is poured directly into a cuvette.

Advancing Hyperspectral Image Analysis with CTNet: An Approach with the Fusion of Spatial and Spectral Features.

Hyperspectral image classification remains challenging despite its potential due to the high dimensionality of the data and its limited spatial resolution. To address the limited data samples and less spatial resolution issues, this research paper presents a two-scale module-based CTNet (convolutional transformer network) for the enhancement of spatial and spectral features. In the first module, a virtual RGB image is created from the HSI dataset to improve the spatial features using a pre-trained ResNeXt model trained on natural images, whereas in the second module, PCA (principal component analysis) is applied to reduce the dimensions of the HSI data. After that, spectral features are improved using an EAVT (enhanced attention-based vision transformer). The EAVT contained a multiscale enhanced attention mechanism to capture the long-range correlation of the spectral features. Furthermore, a joint module with the fusion of spatial and spectral features is designed to generate an enhanced feature vector. Through comprehensive experiments, we demonstrate the performance and superiority of the proposed approach over state-of-the-art methods. We obtained AA (average accuracy) values of 97.87%, 97.46%, 98.25%, and 84.46% on the PU, PUC, SV, and Houston13 datasets, respectively.

SEG-SLAM: Dynamic Indoor RGB-D Visual SLAM Integrating Geometric and YOLOv5-Based Semantic Information.

Simultaneous localisation and mapping (SLAM) is crucial in mobile robotics. Most visual SLAM systems assume that the environment is static. However, in real life, there are many dynamic objects, which affect the accuracy and robustness of these systems. To improve the performance of visual SLAM systems, this study proposes a dynamic visual SLAM (SEG-SLAM) system based on the orientated FAST and rotated BRIEF (ORB)-SLAM3 framework and you only look once (YOLO)v5 deep-learning method. First, based on the ORB-SLAM3 framework, the YOLOv5 deep-learning method is used to construct a fusion module for target detection and semantic segmentation. This module can effectively identify and extract prior information for obviously and potentially dynamic objects. Second, differentiated dynamic feature point rejection strategies are developed for different dynamic objects using the prior information, depth information, and epipolar geometry method. Thus, the localisation and mapping accuracy of the SEG-SLAM system is improved. Finally, the rejection results are fused with the depth information, and a static dense 3D mapping without dynamic objects is constructed using the Point Cloud Library. The SEG-SLAM system is evaluated using public TUM datasets and real-world scenarios. The proposed method is more accurate and robust than current dynamic visual SLAM algorithms.

Precision Balance Assessment in Parkinson's Disease: Utilizing Vision-Based 3D Pose Tracking for Pull Test Analysis.

Postural instability is a common complication in advanced Parkinson's disease (PD) associated with recurrent falls and fall-related injuries. The test of retropulsion, consisting of a rapid balance perturbation by a pull in the backward direction, is regarded as the gold standard for evaluating postural instability in PD and is a key component of the neurological examination and clinical rating in PD (e.g., MDS-UPDRS). However, significant variability in test execution and interpretation contributes to a low intra- and inter-rater test reliability. Here, we explore the potential for objective, vision-based assessment of the pull test (vPull) using 3D pose tracking applied to single-sensor RGB-Depth recordings of clinical assessments. The initial results in a cohort of healthy individuals (n = 15) demonstrate overall excellent agreement of vPull-derived metrics with the gold standard marker-based motion capture. Subsequently, in a cohort of PD patients and controls (n = 15 each), we assessed the inter-rater reliability of vPull and analyzed PD-related impairments in postural response (including pull-to-step latency, number of steps, retropulsion angle). These quantitative metrics effectively distinguish healthy performance from and within varying degrees of postural impairment in PD. vPull shows promise for straightforward clinical implementation with the potential to enhance the sensitivity and specificity of postural instability assessment and fall risk prediction in PD.