Smartphone Photographs Research Articles

Socio-spatialities and affective atmospheres of COVID-19: A visual essay

The COVID-19 crisis has generated an intensity of feeling globally, as people’s everyday spatial and embodied practices have been continually disrupted and fraught with anxiety and uncertainty. In this visual essay, I present and engage with smartphone photographs of public spaces in the Australian cities of Canberra and Sydney that I have accumulated as a ‘COVID Life’ archive. The photographs record my everyday experiences in and through spaces I inhabited and through which I moved. I have selected some of the images and provide a reflective analysis that draws on the concept of affective atmospheres to consider the sociomaterialities and spatialities evident in the images. I describe how the assemblages of people, things, place and space featured in these images generated a range of thoughts and feelings: both in the moment of capturing the images and in reviewing them at a later time as part of an archive of COVID memorialisation.

Feasibility study of mobile phone photography as a possible outcome measure of systemic sclerosis-related digital lesions.

Clinical trials assessing systemic sclerosis (SSc)-related digital ulcers have been hampered by a lack of reliable outcome measures of healing. Our objective was to assess the feasibility of patients collecting high-quality mobile phone images of their digital lesions as a first step in developing a smartphone-based outcome measure. Patients with SSc-related digital (finger) lesions photographed one or more lesions each day for 30 days using their smartphone and uploaded the images to a secure Dropbox folder. Image quality was assessed using six criteria: blurriness, shadow, uniformity of lighting, dot location, dot angle and central positioning of the lesion. Patients completed a feedback questionnaire. Twelve patients returned 332 photographs of 18 lesions. Each patient sent a median of 29.5 photographs [interquartile range (IQR) 15-33.5], with a median of 15 photographs per lesion (IQR 6-32). Twenty-two photographs were duplicates. Of the remaining 310 images, 256 (77%) were sufficiently in focus; 268 (81%) had some shadow; lighting was even in 56 (17%); dot location was acceptable in 233 (70%); dot angle was ideal in 107 (32%); and the lesion was centred in 255 (77%). Patient feedback suggested that 6 of 10 would be willing to record images daily in future studies, and 9 of 10 at least one to three times per week. Taking smartphone photographs of digital lesions was feasible for most patients, with most lesions in focus and central in the image. These promising results will inform the next research phase (to develop a smartphone monitoring application incorporating photographs and symptom tracking).

Use of smartphone photos to document the oral care status of nursing home residents.

Investigate whether clinically assessed oral care status by an external dentist can be replicated using smartphone photographs. Many people with care needs have poor oral hygiene. To help maintain oral health, caregivers require a simple way of communicating oral care status to the dental team. Cross-sectional study in which smartphone photographs of the oral and prosthetic care situation of N=50 nursing-home patients (mean age 85.6 [SD 6] years, 74% women) were assessed by two blinded, external examiners (D1/D2) and compared with clinical assessments made by the onsite dentist who also took the photographs (D0). Intraoral care status was clinically evaluated using the modified Plaque Index for Long-Term Care (PI-LTC) and Denture Hygiene Index (DHI). The intraclass correlation coefficient (ICC) determined correlations between clinical and photographic findings. The ICC was assessed for the PI-LTC (upper jaw, n=41; lower jaw, n=49) and DHI (upper jaw, n=25; lower jaw, n=18). The DHI showed excellent reliability between clinical assessment and smartphone evaluation for the determination of positive surfaces (ICC: upper 0.91 [95% confidence interval (CI) 0.83-0.96], P < 0.001; lower 0.95 [95% CI 0.89-0.98], P < 0.001). The PI-LTC showed good reliability between clinical assessment (D0) and D1/D2 for determination of vestibular plaque (ICC: upper 0.84 [95% CI 0.74-0.91], P < 0.001; lower 0.87 [95% CI 0.79-0.92], P < 0.001). Although limited, this pilot study demonstrated that standardised smartphone photographs can simply communicate the intraoral care status of patients who have difficulties accessing their dentist. This should be considered when planning improved communication between dentists, care recipients, and their support network.

Urgent Virtual Eye Assessments During the COVID-19 Pandemic.

PurposeWe aimed to evaluate the effectiveness and safety of a virtual eye assessment triage system implemented in response to COVID-19.Patients and MethodsWe conducted a retrospective cross-sectional study using a consecutive sample of all virtual assessments conducted from March 24 to June 7, 2020 at a single ophthalmology center in Toronto, ON, Canada. Visual acuity and smartphone photographs were uploaded to an electronic assessment website. All patients were virtually triaged to an email or phone consult. Patient outcomes and satisfaction were assessed with a quality assurance survey. Primary outcome measures were the incidence of unplanned additional in-person visits and changes in treatment.ResultsWe performed 1535 virtual assessments. Of the triage pathways, 15% received an email consult only and 85% received a phone consult. Subsequently, 15% required an in-person assessment, 3% were referred elsewhere, and 0.1% were sent to the emergency. Presentations were most commonly cornea (52%) and retina (25%). They were non-urgent in 68% of cases and no pharmacologic treatment was required for 49%. Of 397 patients that responded out of 653 patients surveyed, 4% had an unplanned additional visit to the emergency, after which two patients underwent urgent retinal surgery and one patient underwent urgent glaucoma surgery. Two patients (0.5%) had a minor change in treatment.ConclusionAs routine regular in-person visits were not possible during the COVID-19 lockdown, virtual eye assessments provided an opportunity to triage patients. Virtual assessments have the potential to reduce in-person visits, but caution must be exercised to not miss vision-threatening conditions.

Measuring PM2.5 Concentrations from a Single Smartphone Photograph

PM2.5 participates in light scattering, leading to degraded outdoor views, which forms the basis for estimating PM2.5 from photographs. This paper devises an algorithm to estimate PM2.5 concentrations by extracting visual cues and atmospheric indices from a single photograph. While air quality measurements in the context of complex urban scenes are particularly challenging, when only a single atmospheric index or cue is given, each one can reinforce others to yield a more robust estimator. Therefore, we selected an appropriate atmospheric index in various outdoor scenes to identify reasonable cue combinations for measuring PM2.5. A PM2.5 dataset (PhotoPM-daytime) was built and used to evaluate performance and validate efficacy of cue combinations. Furthermore, a city-wide experiment was conducted using photographs crawled from the Internet to demonstrate the applicability of the algorithm in large-area PM2.5 monitoring. Results show that smartphones equipped with the developed method could potentially be used as PM2.5 sensors.

Creating a secure clinical 'Bring Your Own Device' BYOD photography service to document and monitor suspicious lesions in the lid oncology clinic.

BackgroundOphthalmic examinations are mostly documented using sketches and written descriptions. Improvements in app security and IT infrastructure mean that high-quality anterior segment photographs can be routinely collected with smartphones alone. The lid oncology team relied on pre-operative formal slit-lamp imaging in the one-stop biopsy clinic, a lengthy process with capacity limitations, that risked delays to care.MethodsA Bring Your Own Device (BYOD) photography service was developed through a series of iterations and collaborations. Healthcare Assistants took photographs on iPhone SE with Quikvue lens attachments in Pando app. Lesions requiring a slit lamp were photographed by the doctor. Images were uploaded to the patient record twice weekly. The service was evaluated using time–motion studies, imaging quality and utility grading, and patient feedback.ResultsBYOD photography saved lid oncology patients 41 min (one-third of total appointment time) and reduced delays to treatment to zero. A patient survey reflected the acceptability of the service, with 100% feeling photography was important at every visit. In terms of utility, 97.5% of smartphone images were suitable for monitoring lesions and making management decisions. The management plan based on the smartphone photographs was consistent with the management plan formulated face-to-face in clinic in 92.5% images.ConclusionBYOD photography has replaced formal slit-lamp imaging in the lid oncology service. This sustainable, cost-effective BYOD solution requires little training and can be adapted, reproduced, and scaled globally. BYOD photography can offer detailed records to monitor progress, contribute to remote care models, improve patient experience, and reducing medical error.

Estimating High-Resolution PM2.5 Concentrations by Fusing Satellite AOD and Smartphone Photographs Using a Convolutional Neural Network and Ensemble Learning

Aerosol optical depth (AOD) data derived from satellite products have been widely used to estimate fine particulate matter (PM2.5) concentrations. However, existing approaches to estimate PM2.5 concentrations are invariably limited by the availability of AOD data, which can be missing over large areas due to satellite measurements being obstructed by, for example, clouds, snow cover or high concentrations of air pollution. In this study, we addressed this shortcoming by developing a novel method for determining PM2.5 concentrations with high spatial coverage by integrating AOD-based estimations and smartphone photograph-based estimations. We first developed a multiple-input fuzzy neural network (MIFNN) model to measure PM2.5 concentrations from smartphone photographs. We then designed an ensemble learning model (AutoELM) to determine PM2.5 concentrations based on the Collection-6 Multi-Angle Implementation of Atmospheric Correction AOD product. The R2 values of the MIFNN model and AutoELM model are 0.85 and 0.80, respectively, which are superior to those of other state-of-the-art models. Subsequently, we used crowdsourced smartphone photographs obtained from social media to validate the transferability of the MIFNN model, which we then applied to generate smartphone photograph-based estimates of PM2.5 concentrations. These estimates were fused with AOD-based estimates to generate a new PM2.5 distribution product with broader coverage than existing products, equating to an average increase of 12% in map coverage of PM2.5 concentrations, which grows to an impressive 25% increase in map coverage in densely populated areas. Our findings indicate that the robust estimation accuracy of the ensemble learning model is due to its detection of nonlinear correlations and high-order interactions. Furthermore, our findings demonstrate that the synergy of smartphone photograph-based estimations and AOD-based estimations generates significantly greater spatial coverage of PM2.5 distribution than AOD-based estimations alone, especially in densely populated areas where more smartphone photographs are available.

Parent-provided photographs as an outcome measure for childhood chalazia

To determine whether smartphone photographs of children's eyelids are reliable for diagnosing the presence of chalazia. In this prospective cross-sectional study, 60 participants, 7 months to 16.5 years of age, at four sites were enrolled; all participants had a chalazion measuring at least 2 mm on at least one eyelid based on an in-person clinical examination by a pediatric eye care professional. Smartphone photographs taken by the parent during the office visit were uploaded to the study website. A masked reader assessed each photograph for the presence or absence of chalazia; results were compared with the gold standard clinical examination results. Sensitivity and specificity for the presence of chalazion by eyelid were calculated. Photographs were available for 240 eyelids; 85 had at least one chalazion and 155 were without a chalazion based on clinical examination. The masked reader correctly classified 68 of 85 eyelids with at least one chalazion and 151 of 155 eyelids without chalazia for a sensitivity of 80% (95% CI, 72%-86%) and a specificity of 97% (95% CI, 94%-99%). Sensitivity improved to 89% for chalazia 5 mm or larger and 94% when superficially located within the eyelid. Parental smartphone photographs appear to be useful in assessing chalazia in children as an alternative to in-office follow-up examinations. These photographs may be a valuable outcome measure in future clinical trials of chalazia treatment, especially when assessing larger lesions.

Smartphone–Camera–Based Water Reflectance Measurement and Typical Water Quality Parameter Inversion

Crowdsourced data from smart devices play an increasingly important role in water quality monitoring. However, guaranteeing and evaluating crowdsourced data quality is a key issue. This study aims to extract more accurate water reflectance data from smartphone photographs with variable exposure parameters, and to test the usability of these data in deriving water quality parameters. A set of low–cost reference cards was designed to be placed in the center of the photograph near the water surface, and a calculation model was proposed to convert the photograph digital numbers (DNs) to water reflectance. A nonlinear DN–to–reflectance model was constructed using the inherent reflectance and DN of the reference card in the photograph. Then, the reflectance of the water surface in the same photograph was estimated. During the evaluation of this scheme in seven different waterbodies with 112 sampling sites, small differences were observed between the estimated and measured remote sensing reflectance; the average unbiased relative errors (AUREs) for the red, green, and blue bands were 25.7%, 29.5%, and 35.2%, respectively, while the RMSEs for the three bands were 0.0032, 0.0051, 0.0031, respectively. The derived water reflectance data were used to retrieve the Secchi–disk depth (Zsd) and turbidity, with accuracies of 72.4% and 60.2%, respectively. The results demonstrate that the proposed method based on the smartphone camera can be used to derive the remote sensing reflectance and water quality parameters effectively with acceptable accuracy.

Size and Perception of Facial Features with Selfie Photographs, and Their Implication in Rhinoplasty and Facial Plastic Surgery.

Patients increasingly use photographs taken with a front-facing smartphone camera-"selfies"-to discuss their goals with a plastic surgeon. The purpose of this study was to quantify changes in size and perception of facial features when taking a selfie compared to the gold standard of clinical photography. Thirty volunteers took three series of photographs. A 12-inch and 18-inch series were taken with a front-facing smartphone camera, and the 5-foot clinical photography series was taken with a digital single-lens reflex camera. Afterward, subjects filled out the FACE-Q inventory, once when viewing their 12-inch selfies and once when viewing their clinical photographs. Measurements were taken of the nose, lip, chin, and facial width. Nasal length was, on average, 6.4 percent longer in 12-inch selfies compared to clinical photography, and 4.3 percent longer in 18-inch selfies compared to clinical photography. The alar base width did not change significantly in either set of selfies compared to clinical photography. The alar base to facial width ratio represents the size of the nose in relation to the face. This ratio decreased 10.8 percent when comparing 12-inch selfies to clinical photography (p < 0.0001) and decreased 7.8 percent when comparing 18-inch selfies to clinical photography (p < 0.0001). This study quantifies the change in facial feature size/perception seen in previous camera-to-subject distance studies. With the increasing popularity of front-facing smartphone photographs, these data allow for a more precise conversation between the surgeon and the patient. In addition, the authors' findings provide data for manufacturers to improve the societal impact of smartphone cameras. Diagnostic, III.

Mild to moderate atopic dermatitis severity can be reliably assessed using smartphone-photographs taken by the patient at home: A validation study.

The use of photographs to diagnose and monitor skin diseases is gaining ground. To investigate the validity and reliability of photographic assessments of atopic dermatitis (AD) severity. AD severity was evaluated in the clinic by two assessors using the Eczema Area and Severity Index (EASI), SCOring Atopic Dermatitis (SCORAD), and Investigator's Global Assessment (IGA). Participants photographed the lesions with their own smartphone and completed a questionnaire about the extent of eczema the same day from home. The photographs were assessed twice with an 8 weeks interval by five dermatologists experienced in photographic evaluations. Intraclass correlation coefficients (ICC) with 95% confidence interval (CI) were applied. Seventy-nine participants were enrolled. The ICC between clinical EASI and photographic EASI was 0.88 (95% CI 0.81-0.93), and 0.86 (0.70-0.93) between clinical SCORAD and photographic SCORAD. Perfect agreement between clinical IGA and photograph IGA was observed for 62%, with the difference between the two never deviating with more than 1 score. The inter-rater ICC for photographic EASI and photographic SCORAD, respectively, was 0.90 (0.85-0.94), and 0.96 (0.91-0.98). The intra-rater agreements between the first and second assessments varied from 0.95 to 0.98 for photographic EASI, and from 0.86 to 0.94 for photographic SCORAD. There was high agreement between mild to moderate AD severity assessed clinically and based on smartphone photographs. Further, the photographic assessments can be reproduced with high reliability.

Extraction of Aerosol Optical Extinction Properties From a Smartphone Photograph to Measure Visibility

Extraction of Aerosol Optical Extinction Properties From a Smartphone Photograph to Measure Visibility

Using smartphone photographs of the Moon to acquaint students with non-Euclidean geometry

Non-Euclidean geometry can be taught to students using astronomical images. By using photographs of the Moon taken with a smartphone through a simple telescope, we were able to introduce these concepts to high-school students and lower-level college students. We teach students how to calculate lengths of mountain ranges or areas of craters on the Moon's surface and introduce ideas of geodesics and spherical triangles. Students can see that accurate measurements cannot be obtained using flat geometry. Instead, by using three-dimensional curved geometry, estimates of lengths and areas can be computed with less than 4% error.

Feasibility assessment of infectious keratitis depicted on slit-lamp and smartphone photographs using deep learning

BackgroundThis study aims to investigate how infectious keratitis depicted on slit-lamp and smartphone photographs can be reliably assessed using deep learning. Materials and MethodsWe retrospectively collected a dataset consisting of 5,673 slit-lamp photographs and 400 smartphone photographs acquired on different subjects. Based on multiple clinical tests (e.g., cornea scraping), these photographs were diagnosed and classified into four categories, including normal (i.e., no keratitis), bacterial keratitis (BK), fungal keratitis (FK), and herpes simplex virus stromal keratitis (HSK). We preprocessed these slit-lamp images into two separate subgroups: (1) global images and (2) regional images. The cases in each group were randomly split into training, internal validation, and independent testing sets. Then, we implemented a deep learning network based on the InceptionV3 by fine-tuning its architecture and used the developed network to classify these slit-lamp images. Additionally, we investigated the performance of the InceptionV3 model in classifying infectious keratitis depicted on smartphone images. We, in particular, clarified whether the computer model trained on the global images outperformed the one trained on the regional images. The quadratic weighted kappa (QWK) and the receiver operating characteristic (ROC) analysis were used to assess the performance of the developed models. ResultsOur experiments on the independent testing sets showed that the developed models achieved the QWK of 0.9130 (95% CI: 88.99–93.61%) and 0.8872 (95% CI: 86.13–91.31%), and 0.5379 (95% CI, 48.89–58.69%) for the global images, the regional images, and the smartphone images, respectively. The area under the ROC curves (AUCs) were 0.9588 (95% CI: 94.28–97.48%), 0.9425 (95% CI: 92.35–96.15%), and 0.8529 (95% CI: 81.79–88.79%) for the same test sets, respectively. ConclusionThe deep learning solution demonstrated very promising performance in assessing infectious keratitis depicted on slit-lamp photographs and the images acquired by smartphones. In particular, the model trained on the global images outperformed that trained on the regional images.

Learning from Scarce Information: Using Synthetic Data to Classify Roman Fine Ware Pottery.

In this article, we consider a version of the challenging problem of learning from datasets whose size is too limited to allow generalisation beyond the training set. To address the challenge, we propose to use a transfer learning approach whereby the model is first trained on a synthetic dataset replicating features of the original objects. In this study, the objects were smartphone photographs of near-complete Roman terra sigillata pottery vessels from the collection of the Museum of London. Taking the replicated features from published profile drawings of pottery forms allowed the integration of expert knowledge into the process through our synthetic data generator. After this first initial training the model was fine-tuned with data from photographs of real vessels. We show, through exhaustive experiments across several popular deep learning architectures, different test priors, and considering the impact of the photograph viewpoint and excessive damage to the vessels, that the proposed hybrid approach enables the creation of classifiers with appropriate generalisation performance. This performance is significantly better than that of classifiers trained exclusively on the original data, which shows the promise of the approach to alleviate the fundamental issue of learning from small datasets.

Remote Rating of Atopic Dermatitis Severity Using Photo-Based Assessments: Proof-of-Concept and Reliability Evaluation.

BackgroundDigital imaging of dermatological patients is a novel approach to remote assessment and has recently become more relevant since telehealth and remote decentralized clinical trials are gaining ground.ObjectiveWe aimed to investigate whether photographs taken by a smartphone are of adequate quality to allow severity assessments to be made and to explore the usefulness of an established atopic dermatitis severity assessment instrument on photograph evaluation.MethodsDuring scheduled visits in a previously published study, the investigating doctor evaluated the severity of atopic dermatitis using the Scoring AD (SCORAD) index and took photographs of the most representative lesions (target lesions) with both a smartphone and a digital single-lens reflex camera (DSLR). The photographs were then assessed by 5 dermatologists using the intensity items of the SCORAD (iSCORAD), which consists of erythema, oedema/papulation, excoriations, lichenification, oozing/crusts, and dryness (scale 0-3, maximum score 18). The mean iSCORAD of the photographs was calculated and compared with in-person assessments using Pearson correlation and Bland-Altman plots. Intraclass correlation coefficients were used for interrater reliability.ResultsA total of 942 photographs from 95 patients were assessed. The iSCORAD based on smartphone photographs correlated strongly with the evaluations performed in person (iSCORAD: r=0.78, P<.001; objective SCORAD: r=0.81, P<.001; and total SCORAD: r=0.78, P<.001). For iSCORAD specifically, a Bland-Altman plot showed a difference in mean score of 1.31 for in-person and remote iSCORAD. In addition, the interrater agreement between the 5 rating dermatologists was 0.93 (95% CI 0.911-0.939). A total of 170 lesions were photographed, and the difference in mean scores was 1.32, 1.13, and 1.43 between in-person and remote evaluations based on photographs taken by a DSLR camera, a smartphone without flash, and a smartphone with flash, respectively.ConclusionsIn terms of quality, remote atopic dermatitis severity assessments based on photographs are comparable to in-person assessments, and smartphone photos can be used to assess atopic dermatitis severity to a similar degree as photographs from a DSLR camera. Further, the variation in how the dermatologists in this study rated the iSCORAD based on the photographs was very low.

Resident Perspectives 56–3

Resident Perspectives 56–3

What Is the Best Way for Patients to Take Photographs of Medical Images (Radiographs, CT, and MRI) Using a Smartphone?

Teleradiology has become one of the most important approaches to virtual clinical diagnosis; its importance has only grown during the coronavirus 2019 pandemic. In developing countries, asking patients to take photographs of their images using a smartphone can facilitate the process and help keep its costs down. However, the images taken by patients with smartphones often are of poor quality, and there is no regulation or standard instruction about how to use smartphones to take photographs of medical examination images effectively. These problems limit the use of smartphones in remote diagnosis and treatment. To formulate a set of guidelines for the most appropriate and effective use of smartphones to capture images (radiographs, CT images, and MR images), and to determine whether these guidelines are more effectively adopted by patients of differing ages and genders. In this prospective study, a set of step-by-step instructions was created with the goal of helping patients take better smartphone photographs of orthopaedic diagnostic images for transfer to telemedicine services. Following the advice of surgeons, experts in smartphone technology, imaging experts, and suggestions from patients, the instructions were modified based on clinical experience and finalized with the goals of simplicity, clarity, and convenience. Potentially eligible patients were older than 18 years, had no cognitive impairment, and used smart phones. Based on that, 256 participants (patients or their relatives and friends) who visited the orthopaedic department of our hospital from June to October 2020 potentially qualified for this study. A total of 11% (29) declined to participate, leaving 89% (227) for analysis here. Their mean age was 36 ± 11 years, 50% were women (113 of 227), and the patient himself/herself represented in 34% (78 of 227) of participants while relatives or friends of patients made up 66% (149 of 227) of the group. In this study, the diagnoses included spinal stenosis (47% [107 of 227]), disc herniation without spinal stenosis (31% [71 of 227]), vertebral fractures (14% [32 of 227]), and other (7% [17 of 227]). Each study participant first took photographs of their original medical images based on their own knowledge of how to use the smartphone camera function; each participant then took pictures of their original images again after receiving our instructional guidance. Three senior spine surgeons (YZ, TQL, TCM) in our hospital analyzed, in a blinded manner, the instructed and uninstructed imaging files based on image clarity (the content of the image is complete, the text information in the image is clearly visible, there is neither reflection nor shadow in the image) and image position (it is not tilted, curled, inverted, or reversed). If either of these conditions was not satisfied, the picture quality was deemed unacceptable; two of three judges' votes determined the outcome. Interobserver reliability with kappa values for the three judges were 0.89 (YZ versus TQL), 0.92 (YZ versus TCM), and 0.90 (TQL versus TCM). In this study, the overall proportion of smartphone medical images deemed satisfactory increased from 40% (91 of 227) for uninstructed participants to 86% (196 of 227) for instructed participants (risk ratio 2.15 [95% CI 1.82 to 2.55]; p<0.001). The proportion of acceptable-quality images in different age groups improved after instruction, except for in patients aged 51 years or older (3 of 17 uninstructed participants versus 8 of 17 instructed participants; RR 2.67 [95% CI 0.85 to 8.37]; p = 0.07). The proportion of acceptable-quality images in both genders improved after instruction, but there was no difference between the genders. We believe our guidelines for patients who wish to take smartphone photographs of their medical images will decrease image transmission cost and facilitate orthopaedic telemedicine consultations. However, it appears that patients older than 50 years are more likely to have difficulty with this approach, and if so, they may benefit from more hands-on assistance from clinic staff or younger relatives or friends. The degree to which our findings are culture-specific should be verified by other studies in other settings, but on the face of it, there is little reason to believe our findings would not generalize to a reasonable degree. Other studies in more heterogeneous populations should also evaluate factors related to levels of educational attainment and wealth differences, but in the meantime, our findings can give clinical teams an idea of which patients may need a little extra assistance. Level II, therapeutic study.

Proximal Sensing of Nitrogen Needs by Spring Wheat

Canopy nitrogen (N) status relates strongly to canopy chlorophyll content and the strength of green color. Proximal photograph by RGB camera was used to select green features that has the potential to assess N content at leaf of plant as a function of its the greenness. We proposed the development of it as a tool for sensing nitrogen content in spring wheat (Triticum aestivum). Image processing algorithm was programed calibrated and validated wheat %N%N. Nitrogen uptake =%N × canopy dry matter was harvested and calculated using simulated dry matter by DSSAT model. The data replicated laboratory measurements. A linear Lab vs Camera model displayed a unit slope with r2 = 0.93. Increase of dry matter was successfully surrogated by days after emergence and used as abscissa for inverse logistic model of critical nitrogen level. It decreased gradually from about 6% to 2% as days after emergence increased from 0 to 110 days. Maximum N uptake calculated from photo and laboratory was 324 Kg ha−1 and 318 Kg ha−1 respectively suggesting insignificant difference. Physiological N-use efficiency (i.e., canopy weight/N weight) was 52 and 78 kg canopy dry weight per 1 kg N for early and late-ripening cultivars, respectively. The determination of N application based on the smartphone photograph proved to be useful by saving on time and expenses for growers who have access to smartphones and can use them for N application and management.

Bedside Detection of Carbapenemase-Producing Pathogens with Plasmonic Nanosensors

Providing the right antibiotic treatment as soon as possible is crucial to improve sepsis outcomes. In this context, infections by bacteria resistant to β-lactams can be deadly. Current approaches for identifying these pathogens are longsome and require complex infrastructure. Here we introduce a diagnostic kit for detecting carbapenemase-producing pathogens at the bedside. It generates colored tests that are easily distinguished by eye or by analyzing a smartphone photograph. The color is generated by gold nanoparticles, which have been designed to detect β-lactamase activity robustly and with ultra-high sensitivity. Neither microbial culture nor specialized equipment are required. The kit can detect infections by carbapenemase-producing bacteria directly from urine samples within 2.5 h, even when these contain an excess of interfering bacteria. It can also detect them in sputum samples within 3 h. The rapid assay time coupled with the ability to detect hazardous infections enables the personalization of antimicrobial treatments much faster than a full antibiotic susceptibility test, which often takes several days to be accomplished. This kit can also be implemented in resource-constrained scenarios where other options for antimicrobial susceptibility testing may not be available.