Ambient Light Conditions Research Articles

Disentangling effects of multiple agricultural stressors on benthic and hyporheic nitrate uptake

Agricultural land use alters nitrate (NO3–) uptake dynamics in streams, but the specific mechanisms linking individual agricultural stressors to benthic and hyporheic uptake remain unclear. Using stream-side mesocosms and 15N-nitrate additions, we examined the individual and combined effects of fine sediment (FS) and augmented light and phosphorus levels (L&P) on benthic and hyporheic NO3– uptake rates. In absence of FS, L&P stimulated uptake of autotrophic and heterotrophic biofilms, leading to a 12- and 7-fold increase in the benthic and hyporheic compartments, respectively. Under ambient light and nutrient conditions, FS reduced by 3-fold benthic uptake, but effects were not significant. Conversely, in the hyporheic compartment, FS induced anoxic conditions, likely stimulating denitrification and causing a 14-fold increase in hyporheic uptake. When these stressors were combined, they did not interact in the benthic compartment. Conversely, in the hyporheic compartment they interacted antagonistically, with L&P diminishing the increase in uptake induced by FS. Our results indicate that the previously observed increase of whole-stream NO3– uptake in agricultural streams is attributable to nutrients and light stimulating benthic uptake, while fine sediment effects and the role of the hyporheic compartment to total uptake are modest. Moreover, the finding that stressor interactions vary with ecosystem compartments calls for a consideration of all compartments and their contribution to whole-system functioning in multiple stressor studies. We are beginning to understand how multiple interacting stressors affect stream functioning, but more mechanistic evidence is needed to disentangle whether additive or non-additive effects prevail in human-altered ecosystems.

Bridging the Gap Between Vitiligo Segmentation and Clinical Scores.

Quantitative evaluation of vitiligo is crucial for assessing treatment response. Dermatologists evaluate vitiligo regularly to adjust their treatment plans, which requires extra work. Furthermore, the evaluations may not be objective due to inter- and intra-assessor variability. Though automatic vitiligo segmentation methods provide an objective evaluation, previous methods mainly focus on patch-wise images, and their results cannot be translated into clinical scores for treatment adjustment. Thus, full-body vitiligo segmentation needs to be developed for recording vitiligo changes in different body parts of a patient and for calculating the clinical scores. To bridge this gap, the first full-body vitiligo dataset with 1740 images, following the international vitiligo photo standard, was established. Compared with patch-wise images, full-body images have more complicated ambient light conditions and larger variances in lesion size and distribution. Additionally, in some hand and foot images, skin can be fully covered by either vitiligo or healthy skin. Previous patch-wise segmentation studies completely ignore these cases, as they assume that the contrast between vitiligo and healthy skin is available in each image for segmentation. To address the aforementioned challenges, the proposed algorithm in this study exploits a tailor-made contrast enhancement scheme and long-range comparison. Furthermore, a novel confidence score refinement module is proposed to manage images fully covered by vitiligo or healthy skin. Our results can be converted to clinical scores and used by clinicians. Compared to the state-of-the-art method, the proposed algorithm reduces the average per-image vitiligo involvement percentage error from 3.69% to 1.81%, and the top 10% per-image errors from 23.17% to 8.29%. Our algorithm achieves 1.17% and 3.11% for the mean and max error for the per-patient vitiligo involvement percentage, which is better than an experienced dermatologist's naked-eye evaluation.

Assessing heart rate and blood pressure estimation from image photoplethysmography using a digital blood pressure meter

This study presents a non-contact approach to measuring heart rate and blood pressure using an image photoplethysmography (iPPG) signal, and compares the results to those from an oscillometric blood pressure meter. Facial videos of 100 subjects were recorded via a webcam under ambient lighting conditions to extract iPPG signals. The results revealed a strong correlation between the heart rate derived from iPPG and that obtained from an oscillometric blood pressure meter. In addition, a continuous wavelet transform images with a 6-s duration were used as input for a custom convolutional neural network model, providing the most accurate blood pressure estimation. The proposed method received a grade A for diastolic and grade B for systolic blood pressure based on the British Hypertension Society's criteria. It also met the standards set by the Association for the Advancement of Medical Instrumentation. This non-contact framework shows promising potential for efficient screening purposes.

Effect of Human Adenovirus Type 35 Concentration on Its Inactivation and Sorption on Titanium Dioxide Nanoparticles.

Removal of pathogenic viruses from water resources is critically important for sanitation and public health. Nanotechnology is a promising technology for virus inactivation. In this paper, the effects of titanium dioxide (TiO2) anatase nanoparticles (NPs) on humanadenovirustype 35 (HAdV-35) removal under static and dynamic (with agitation) batch conditions were comprehensively studied. Batch experiments were performed at room temperature (25°C) with and without ambient light using three different initial virus concentrations. The virus inactivation experimental data were satisfactorily fitted with a pseudo-first-order expression with a time-dependent rate coefficient. The experimental results demonstrated that HAdV-35 sorption onto TiO2 NPs was favored with agitation under both ambient light and dark conditions. However, no distinct relationships between virus initial concentration and removal efficiency could be established from the experimental data.

Effective flame detection and classification using deep feature of QuickDenseNet and Ensemble Score Voted SVM

Fire poses a significant threat to both lives and property, necessitating effective early detection measures. Despite challenges in identifying smoke and fire in their initial stages, we have devised a cost-efficient visual detection system. Early fire detection enhances its potential effectiveness. CCTV surveillance systems are now commonplace in developed countries, serving as tools for periodic monitoring of various locations. However, fluctuating ambient light conditions, camera angles, and seasonal variations can introduce data distortions, occlusions, and impact model accuracy. To address these issues, we’ve implemented a method combining deep learning networks and machine learning strategies for flame detection and direction classification. Our innovative QuickDenseNet extracts dense features from segmented flame video frames. We introduce the Ensemble Score Voted SVM (ESV-SVM), employing SVM as the primary learner and score voting as the auxiliary learner. Our approach is rigorously evaluated through simulations, measuring accuracy and various Key Performance Indices (KPIs), including Precision, F1-score, Recall, Correlation, Error, FPR, and Correlation Coefficients. Remarkably, our proposed method achieves an impressive precision rate of approximately 99.5%.

The effects of different lighting conditions on the accuracy of intraoral scanning.

This study aimed to investigate the extent to which intraoral scanning are affected by clinical conditions, and whether ambient lighting and different color temperatures have an impact on the accuracy of intraoral scanner, as well as to evaluate scanning time. Twelve different environments were created using various ambient lighting conditions (0, 500, 1000 and 1500 lux) and color temperatures (white, blue and yellow). A partially edentulous mandibular model with two implants and a three-unit bridge was scanned under each environment until 10 digital models were obtained, and scanning times were recorded using a virtual stopwatch. A 3D analysis was performed on the obtained digital models, and the data were analyzed using a software. The generalized linear model analysis and Tukey multiple comparison test were used to analyse the data (P < .05). The effect of lux, color temperature, and scanning times on RMS data was found to be significant (P < .001). The mean RMS value was the highest in the 0 lux group and the lowest in the 500 lux group. Regarding the color temperature, the highest RMS value was in the white color group and the lowest in the yellow color group. Scanning times were similar among the 0, 500 and 1000 lux groups, with a significant increase in the 1500 lux group. Different ambient lighting conditions and color temperatures have significant effect on the accuracy of intraoral scanning.

Thermal-based gender recognition using drones: advancing biometric recognition in challenging outdoor environments

While biometric recognition typically uses features such as face, fingerprint, and iris to identify individuals, this study focuses on utilising specific characteristics to identify gender. The aim of this article is to propose a procedure for gender recognition under specific conditions. The specific condition addressed is outdoor area monitoring, which presents challenges such as varying lighting conditions and limited camera placement options. To tackle this, a proposed procedure utilises thermal images captured by the drone equipped with a thermal camera. The advantage of thermal images is their independence from ambient light conditions. The captured images are resized and processed using convolutional neural networks (CNNs) (AlexNet, VGG-16, VGG-19) for feature extraction and binary classification. A freely available database of thermal face images is used for training the CNNs, while a own created dataset of thermal images obtained by the drone is used for testing. The findings indicate that the optimised CNNs achieve classification accuracies of 82.4% (VGG-16), 82.9% (AlexNet), and 85.5% (VGG-19). The original contribution of this study lies in demonstrating the suitability of face thermal images obtained through drones for gender recognition purposes.

A shadow enabled non-invasive probe for multi-feature intelligent liquid surveillance system.

Liquid detection probes used to identify the features of liquids show great promise in a variety of important applications. However, some challenges, such as sample contamination by direct contact with the liquid, the requirement of additional signal emitters, and complex fabrication, hindered the development of liquid detection probes. Here, we developed a non-invasive shadow probe (SP) for a multi-feature intelligent liquid surveillance system (ILSS). The self-powered SP with the working mechanism of the shadow effect can detect the features of liquids by analyzing the variations of liquid shadows such as the area, wavelength, and brightness. The exact resolution (5 different colors, 6 different concentrations, 6 different levels, 100% accuracy) and fast response time (0.2 ms) are shown by the SP under ambient light conditions (even in 0.003 sun). The ILSS, which integrated the SPs with signal processing circuits and applied the artificial intelligence (AI) technique, successfully detects and synoptically learns about liquids simultaneously. The in-real time ILSS reaches a test accuracy of 99.3% for 10 types of liquids with multiple features. This work showcases a promising solution for non-invasive multi-feature liquid detection, displaying great potential for future applications.

Multifaceted photoreceptor compositions in dual phototrophic systems – A genomic analysis

For microbes and their hosts, sensing of external cues is essential for their survival. For example, in the case of plant associated microbes, the light absorbing pigment composition of the plant as well as the ambient light conditions determine the well-being of the microbe. In addition to light sensing, some microbes can utilize xanthorhodopsin based proton pumps and bacterial photosynthetic complexes that work in parallel for energy production. They are called dual phototrophic systems. Light sensing requirements in these type of systems are obviously demanding. In nature, the photosensing machinery follows mainly the same composition in all organisms. However, the specific role of each photosensor in specific light conditions is elusive. In this study, we provide an overall picture of photosensors present in dual phototrophic systems. We compare the genomes of the photosensor proteins from dual phototrophs to those from similar microbes with ”single” phototrophicity or microbes without phototrophicity. We find that the dual phototrophic bacteria obtain a larger variety of photosensors than their light inactive counterparts. Their rich domain composition and functional repertoire remains similar across all microbial photosensors. Our study calls further investigations of this particular group of bacteria. This includes protein specific biophysical characterization in vitro, microbiological studies, as well as clarification of the ecological meaning of their host microbial interactions.

Effects of indoor lighting conditions on subjective preferences of task lighting and room aesthetics in an Indian tertiary educational institution

Indoor lighting conditions influence the psychology of pupils in academic institutions and it is expedient to investigate how subjective experiences are modulated by differences in lighting conditions. This study was conducted in a tertiary educational institute in West Bengal, India with twenty-four student participants, divided into three groups of eight each, to assess how the appraisals of task lighting and room aesthetics varied under three ambient lighting conditions. Spread across nine sessions of 1 h each, the groups performed pre-defined tasks and recorded their responses upon copies of a formulated questionnaire at the conclusion of each session. Moreover, blood pressure and heart rate measurements of the participants were made at an interval of 20 min. Statistical analysis with Kruskal-Wallis and Friedman tests implied that there were significant differences among the lighting conditions with respect to several dependent variables of task lighting and room aesthetics appraisal. Artificial lighting was deemed to be uniform, pleasant for task performance, and comfortable for book reading. Daylighting with the concomitant window view was construed as brighter, glaring, dramatic, more contrasting, and less uniform. A combination of natural and artificial lighting was bright, mildly contrasting, dramatic, softer in luminosity, and comfortable. Withal, daylighting with the concomitant window view appeared to regulate participants’ blood pressure and heart rate. Thus, this study implies that the balance and holistic synergy between artificial and natural light can create an enlivening and salubrious indoor environment, and building service professionals should conscientiously harness the same for upgrading current practices in indoor lighting.

Impact of color temperature and illuminance of ambient light conditions on the accuracy of complete-arch digital implant scans.

The purpose of the present study was to assess the influence of color temperature and illuminance of ambient light on the accuracy of different intraoral scanners (IOSs) in complete-arch implant scans. An edentulous model with six implants and scan bodies was digitized by using a laboratory scanner (DW-7-140; Dental Wings) to obtain a reference mesh. Fifteen scans were performed employing two intraoral scanners (Trios 4;3Shape A/S and i700; Medit Co) at two illuminances (500 and 1000 lux) and three color temperatures (3200, 4400, and 5600 K). Scanning accuracy was measured by using a 3D metrology software program (Geomagic Control X). Kruskal-Wallis, one-way ANOVA, and pairwise comparison tests were used to analyze the data (α = .05). Significant differences in trueness and precision values were found among the different IOSs under the same ambient lighting condition and among the different lighting conditions for a given IOS (p < .05) except for trueness in i700 groups (p > .05). The influence on the accuracy of color temperature and illuminance varied depending on the intraoral scanner. An optimal ambient scanning light condition was not found; this should be adjusted based on the specific IOS system used. 3200 K of ambient light influences the precision of i700 when performed at 1000 lux, decreasing the accuracy. The variation of color temperature at the same illuminance does not affect the scanning accuracy of TRIOS 4, which obtained better accuracy in all scans at 1000 lux.

Application of a Validated Method for Quantifying Circulating Protoporphyrin IX to the Beacon Trial of Bitopertin in Erythropoietic Protoporphyria

Introduction: Accumulation of the photoreactive heme precursor protoporphyrin IX (PPIX) is a signature of the disease pathophysiology for both erythropoietic protoporphyria (EPP) and X-linked protoporphyria (XLP), and PPIX levels correlate with disease severity and light tolerance. Additionally, an elevated ratio of circulating metal-free PPIX vs. metal-bound PPIX is observed in patients with protoporphyrias, and the extent of metal-free elevation is distinguishable between EPP (85-100%) and XLP (50-85%). Therefore, the accurate determination of metal-free PPIX and metal-bound PPIX is of particular interest in patients with EPP or XLP. Methods: Novel liquid chromatography-tandem mass spectrometry (LC-MS/MS) assays were developed and validated to quantify circulating metal-free and zinc (Zn)-complexed PPIX in whole blood and plasma. Each of the four assays were developed and validated according to applicable regulatory guidance for bioanalytical assay validation with regard to accuracy, precision, sensitivity, and selectivity. The minimum required sample volume for each assay was 200 µL, compared to &amp;gt;1 mL for most currently available protoporphyria-related assays. During bioanalytical method development, rapid degradation of metal-free PPIX (≥90%) and Zn-PPIX (up to 96%) was observed in plasma samples within 24 hours under ambient light conditions. In contrast, whole blood was less susceptible to photodegradation (i.e., ≤20% for both metal-free PPIX and Zn-PPIX) under the same conditions. The most effective method for maintaining sample integrity was reduction of light exposure during sample collection, handling, and processing. Implementation of light-reduction controls was used during method validation and facilitated improved stability of metal-free PPIX and Zn-PPIX in plasma and whole-blood samples. For the metal-free PPIX plasma assay validation, the effect of hemolysis was evaluated due to the apparent sequestration of metal-free PPIX in red blood cells. The bioanalytical work informed specific sample-collection procedures to mitigate instability and hemolysis, such as the use of opaque consumables for sample collection, controls for plasma aliquoting, and operating under low-light conditions for all sample handling and processing. BEACON is a Phase 2, randomized, open-label, parallel-arm trial (ACTRN12622000799752) of approximately 22 participants who will receive oral, once-daily administration of 20 or 60 mg of bitopertin for 6 months. Baseline light tolerance was assessed using 2 patient-reported outcomes measures during a 2- to 4-week screening period. Participants were asked to conduct weekly sun exposure challenges and time to first prodromal symptom (itching, burning, tingling, etc.) was averaged for the screening period. Results: More than 600 clinical samples from 17 participants in the BEACON study have been collected as of 06 June 2023 and analyzed using the validated porphyrin assays. Measured metal-free PPIX baseline values ranged from 1440 - 34,100 ng/mL (2.6 - 61 µM) in whole blood and 177 - 2560 ng/mL (0.31 - 4.5 µM) in plasma ( n=16). Baseline percentage of intra-patient plasma/whole-blood concentrations ranged from 2.5 - 16%. Each participant's baseline values were converted to erythrocyte metal-free PPIX concentrations using measured hematocrit levels and ranged from 3590 - 88400 ng/mL (6.4 - 157 µM). These values were comparable to data previously reported in the literature (6-139 µM 1). Additionally, higher baseline whole-blood metal-free PPIX concentrations were associated with decreased averages for time to prodrome (Table 1). Conclusion: Fully-validated LC-MS/MS assays to quantify metal-free PPIX and Zn-PPIX in plasma and whole blood were used to analyze clinical samples from the BEACON study evaluating bitopertin in EPP. PPIX values were consistent with previously reported values for patients with EPP. The methods also provided improved accuracy and specificity for metal-free vs. metal-complexed PPIX and reduced sample volume requirements. In addition, higher baseline whole-blood PPIX values were associated with decreased light tolerance within the trial population, supporting its use as a measure of disease severity in EPP.

Light wavelength and pulsing frequency affect avoidance responses of Canada geese.

Collisions between birds and aircraft cause bird mortality, economic damage, and aviation safety hazards. One proposed solution to increasing the distance at which birds detect and move away from an approaching aircraft, ultimately mitigating the probability of collision, is through onboard lighting systems. Lights in vehicles have been shown to lead to earlier reactions in some bird species but they could also generate attraction, potentially increasing the probability of collision. Using information on the visual system of the Canada goose (Branta canadensis), we developed light stimuli of high chromatic contrast to their eyes. We then conducted a controlled behavioral experiment (i.e., single-choice test) to assess the avoidance or attraction responses of Canada geese to LED lights of different wavelengths (blue, 483 nm; red, 631nm) and pulsing frequencies (steady, pulsing at 2 Hz). Overall, Canada geese tended to avoid the blue light and move towards the red light; however, these responses depended heavily on light exposure order. At the beginning of the experiment, geese tended to avoid the red light. After further exposure the birds developed an attraction to the red light, consistent with the mere exposure effect. The response to the blue light generally followed a U-shape relationship (avoidance, attraction, avoidance) with increasing number of exposures, again consistent with the mere exposure effect, but followed by the satiation effect. Lights pulsing at 2 Hz enhanced avoidance responses under high ambient light conditions; whereas steady lights enhanced avoidance responses under dim ambient light conditions. Our results have implications for the design of lighting systems aimed at mitigating collisions between birds and human objects. LED lights in the blue portion of the spectrum are good candidates for deterrents and lights in the red portion of the spectrum may be counterproductive given the attraction effects with increasing exposure. Additionally, consideration should be given to systems that automatically modify pulsing of the light depending on ambient light intensity to enhance avoidance.

An ultrasonic-assisted Synthesis, Characterization, and application of Nano-Fe3O4/TiO2 as nano-catalyst for the removal of organic dye by Like-Photo-Fenton reactions

In this research, Fe3O4 nanoparticles were synthesized via a simple co-precipitation method. Subsequently, these nanoparticles were used to synthesize TiO2-Fe3O4 nanocomposites via solvothermal ultrasonic-assisted method. Obtained nanomaterials Fe3O4 and TiO2-Fe3O4 were structurally characterized by UV–visible, XRD, and SEM-EDS. Synthesized nano-catalysts were tested as catalysis of organic dye degradation. Furthermore, an optimization study was conducted to evaluate selected TiO2-Fe3O4 nanocomposites to degrade Methylene Blue (MB) in an aqueous solution through Fenton-like reactions. Several experimental variables, including pH (from 2 to 8), H2O2 concentration (from 10-2 to 5.10-1M), catalyst amount (20 to 80 mg), and target organic compound concentrations, were tested in order to optimize the Fenton-like catalytic activity of the TiO2-Fe3O4 as a nano-catalyst to degrading MB under ambient light conditions (natural sunlight). The results showed that TiO2-Fe3O4 had an efficient degradation rate of up to 85% for MB at the optimal pH 3, H2O2 at 10-1M and 40 mg/L catalyst amount. Also, the results showed the good reusability of TiO2-Fe3O4 as a nanophotocatalyst. Also, based on the results of the scavenging agent experiments, hydroxyl radicals and superoxide radicals play an important role in methylene blue (MB) photodegradation. Finally, a possible mechanism for the photocatalytic degradation of MB was proposed. In this study, the synthesis and application of TiO2-Fe3O4 nanocomposite as a nano-catalyst in the Fenton-like oxidation process demonstrated a promising eco-friendly method for treating organic pollutants in water.

Multiclass weed identification using semantic segmentation: An automated approach for precision agriculture

Accurate identification and categorization of numerous weed species are critical for implementing effective control measures and management methods in precision agriculture. Manual weed treatment is time-consuming, labor-intensive, and poses risks of human pesticide exposure. Therefore, the development of automated weed management systems is highly desirable. This study aims to propose an automated approach for multiclass weed identification using semantic segmentation, with the goal of improving weed control techniques, reducing pesticide usage, and enhancing crop yields in a sustainable manner. To address the research objective, we created a novel multiclass weed dataset, focusing on two weed categories found in a brinjal farm located in Gorakhpur, Uttar Pradesh, India during the 2022 field seasons. The dataset covers various developmental phases and was captured under ambient lighting conditions. Leveraging transfer learning, we evaluated four advanced deep learning models to establish a benchmark for weed identification. Among the evaluated models, the U-Net-based Inception-ReseNetV2 achieved the highest F1-score of 96.78%, while the other three models attained F1-scores above 91.0%. These findings demonstrate the efficacy of the proposed approach in accurately identifying and categorizing weeds in agricultural fields. The results of this research provide a foundation for further investigations on weed detection and localization in field environments. The use of semantic segmentation for multiclass weed identification can significantly enhance the efficiency and effectiveness of weed management operations, resulting in reduced pesticide usage and improved crop yields. By adopting automated weed management systems, farmers can minimize labor requirements, save time, and mitigate the risks associated with human pesticide exposure.

Colorimetric detection of glucose with smartphone-coupled µPADs: harnessing machine learning algorithms in variable lighting environments

Recent advances in microfluidic paper-based analytical devices (µPADs) have shown immense potential for point-of-care testing (POCT) in resource-constrained settings, especially for monitoring glucose levels in patients with diabetes. However, the practical adoption of smartphone-coupled µPADs has been limited due to the variability in ambient lighting conditions, which affects the colorimetric detection. In this study, we have developed a machine learning-based algorithm together with a flash/no-flash technic, adaptable to various µPADs and capable of accurately monitoring plasma glucose levels across a wide range (25 µM to 30 mM), addressing issues related to environmental illumination variations, and shooting angles.The algorithm is implemented in an Android application, Gluco estimator. It utilizes images captured by smartphones and incorporates intelligent feature engineering. The XYZ color space is adopted for device-independent analysis. KI and TMB color indicators are used to enhance resolution for low and high glucose concentrations, respectively. The EBC classifier, combined with a handcrafted feature set, exhibits outstanding performance, achieving an accuracy of 95% and 91% for TMB and KI, respectively. Additionally, multiple linear regression (MLR) yields high reliability among regression models, with R2 values of 0.95 at 0.25 µM to 3 mM and 0.97 at 3–30 mM. This work represents a significant stride toward realizing a portable, high-resolution, and reliable smartphone-coupled µPAD in uncontrolled environments.

Effect of tooth color on the accuracy of intraoral complete arch scanning under different light conditions using a zirconia restoration model

Statement of problemInformation regarding the effect of tooth color under different light conditions on the accuracy of intraoral complete arch scanning is limited. PurposeThe purpose of this in vitro study was to evaluate the effect of color and ambient light conditions on the accuracy of mandibular complete arch scanning with an intraoral scanner (IOS) using a zirconia restoration model with different shades. Material and methodsFive mandible dentition models with zirconia restorations of different shades were fabricated by computer-aided design and computer-aided manufacturing (CAD-CAM). The spectral reflectance and transmittance curves were collected with a spectrophotometer to determine color parameters (Rb, T, S+A, L*, a*, b*, C*, and h). Under 4 different lighting conditions: no light (ZL), natural light (NL), room light (RL), and chair light (CL), each model was scanned 10 times by using an IOS (TRIOS 3). Three-dimensional (3D) deviation analysis and a linear deviation analysis were performed for an accurate quantitative measurement of intraoral scanning. The multivariate test was used to determine significant differences in 3D deviation and linear deviation among groups. The multiple linear regression test was conducted to investigate the relevant independent factors of mean absolute 3D deviation. ResultsThe 3D deviation analysis showed that the mean absolute 3D deviation of 3M2 model scanning was the lowest (P<.001). Moreover, under CL and RL, the accuracy results from the 3M2 model scan were demonstrated as significantly better than the tested scans under other light conditions (P=.021). The result of the linear deviation analysis indicated that the variation in distance was only significant between the bilateral canines (P=.032). Ambient light conditions, C*, and h were factors influencing mean absolute 3D deviation (R2=0.593, P<.001). ConclusionsColor change influenced the accuracy of intraoral mandibular complete arch scanning under different light conditions. This effect may be attributable to the interaction between the ambient light condition and color parameters such as C* and h.

Reconstructing Depth Images for Time-of-Flight Cameras Based on Second-Order Correlation Functions

Depth cameras are closely related to our daily lives and have been widely used in fields such as machine vision, autonomous driving, and virtual reality. Despite their diverse applications, depth cameras still encounter challenges like multi-path interference and mixed pixels. Compared to traditional sensors, depth cameras have lower resolution and a lower signal-to-noise ratio. Moreover, when used in environments with scattering media, object information scatters multiple times, making it difficult for time-of-flight (ToF) cameras to obtain effective object data. To tackle these issues, we propose a solution that combines ToF cameras with second-order correlation transform theory. In this article, we explore the utilization of ToF camera depth information within a computational correlated imaging system under ambient light conditions. We integrate compressed sensing and non-training neural networks with ToF technology to reconstruct depth images from a series of measurements at a low sampling rate. The research indicates that by leveraging the depth data collected by the camera, we can recover negative depth images. We analyzed and addressed the reasons behind the generation of negative depth images. Additionally, under undersampling conditions, the use of reconstruction algorithms results in a higher peak signal-to-noise ratio compared to images obtained from the original camera. The results demonstrate that the introduced second-order correlation transformation can effectively reduce noise originating from the ToF camera itself and direct ambient light, thereby enabling the use of ToF cameras in complex environments such as scattering media.

In-use stability of ready-to-administer daratumumab subcutaneous injection solution in plastic syringes

ObjectiveIn multiple myeloma patients, daratumumab is preferably injected subcutaneously. The summary of product characteristics of daratumumab subcutaneous injection solution specifies physicochemical stability for the prepared syringe for 24 hours at...

Colorimetry-based and smartphone-assisted machine-learning model for quantification of urinary albumin

The presence of albumin in the urine is indicative of kidney damage and can occur due to several underlying conditions, such as diabetes. The concentration of albumin in urine is used for the diagnosis and staging of chronic kidney disease (CKD). In clinical samples, the detection of albumin at lower concentrations is crucial for the early diagnosis and monitoring of CKD. Current urine analyzers precisely quantify albumin but are expensive and difficult to use in point-of-care (PoC) settings. Here, we demonstrate the quantification of albumin concentration in a urine sample using colorimetry. This model presents an accessory-free urine analyzer that uses a smartphone and customized machine-learning algorithms. Here, a urine sample is introduced onto a chemically impregnated dipstick that exhibits a change in color with the amount of albumin. Images of the urine dipsticks are captured using a smartphone camera under different illumination/experimental conditions and are processed to extract changes in the color values arising due to changes in the concentration of urinary albumin. Albumin concentrations are estimated from changes in color values. We used customized machine-learning algorithms to classify albumin concentrations and mitigate the effect of ambient light conditions. The k-nearest neighbor algorithm yielded an average classification accuracy of 96% with a detection limit of 4 mg l−1. The proposed scheme can be extensively used to monitor albumin concentration in PoC settings.