Rapid and stain-free quantification of viral plaque via lens-free holography and deep learning.

A plaque assay-the gold-standard method for measuring the concentration of replication-competent lytic virions-requires staining and usually more than 48 h of runtime. Here we show that lens-free holographic imaging and deep learning can be combined to expedite and automate the assay. The compact imaging device captures phase information label-free at a rate of approximately 0.32 gigapixels per hour per well, covers an area of about 30 × 30 mm2 and a 10-fold larger dynamic range of virus concentration than standard assays, and quantifies the infected area and the number of plaque-forming units. For the vesicular stomatitis virus, the automated plaque assay detected the first cell-lysing events caused by viral replication as early as 5 h after incubation, and in less than 20 h it detected plaque-forming units at rates higher than 90% at 100% specificity. Furthermore, it reduced the incubation time of the herpes simplex virus type 1 by about 48 h and that of the encephalomyocarditis virus by about 20 h. The stain-free assay should be amenable for use in virology research, vaccine development and clinical diagnosis.

Miniaturized wireless sensor enables real-time monitoring of food spoilage.

Food spoilage results in food waste and food-borne diseases. Yet, standard laboratory tests to determine spoilage (mainly volatile biogenic amines) are not performed regularly by supply chain personnel or end customers. Here we developed a poly(styrene-co-maleic anhydride)-based, miniature (2 × 2 cm2) sensor for on-demand spoilage analysis via mobile phones. To demonstrate a real-life application, the wireless sensor was embedded into packaged chicken and beef; consecutive readings from meat samples using the sensor under various storage conditions enabled the monitoring of spoilage. While samples stored at room temperature showed an almost 700% change in sensor response on the third day, those stored in the freezer resulted in an insignificant change in sensor output. The proposed low-cost, miniature wireless sensor nodes can be integrated into packaged foods, helping consumers and suppliers detect spoilage of protein-rich foods on demand, and ultimately preventing food waste and food-borne diseases.

Microplastics retained in stormwater control measures: Where do they come from and where do they go?

Stormwater control measures (SCM) can remove and accumulate microplastics and may serve as a long-term source of microplastics for groundwater pollution because of their potential for downward mobility in subsurface. Furthermore, the number of microplastics accumulated in SCM may have been underestimated as the calculation typically only accounts for microplastics accumulated via episodic stormwater loading and ignores microplastic accumuation via continuous atmospheric deposition. To evaluate the source pathways of accumulated microplastics and their potential for downward mobility to groundwater, we analyzed spatial distributions of microplastics above ground on the canopy around SCM and below ground in the subsurface in and outside the boundaries of fourteen SCM in Los Angeles. Using an exponential model, we link subsurface retardation of microplastics to the median particle size of soil (D50) and land use. Despite receiving significantly more stormwater, microplastic concentrations in SCM at surface depth or subsurface depth were not significantly different from the concentration at the same depth outside the SCM. Similar concentration in and outside of SCM indicates that stormwater is not the sole source of microplastics accumulated in SCM. The high concentration of microplastics on leaves of vegetation in SCM confirms that the contribution of atmospheric deposition is significant. Within and outside the SCM boundary, microplastics are removed within the top 5 cm of the subsurface, and their concentration decreases exponentially with depth, indicating limited potential for groundwater pollution from the microplastics accumulated in SCM. Outside the SCM boundary, the subsurface retardation coefficient decreases with increases in D50, indicating straining of microplastics as the dominant removal mechanism. Inside the boundary of SCM, however, the retardation coefficient was independent of D50, implying that microplastics could have either moved deeper into the filter layer in SCM or that compost, mulch, or organic amendments used in the filter media were pre-contaminated with microplastics. Overall, these results provide insights on microplastics source, accumulation, and downward mobility in SCM.

A deep learning-enabled portable imaging flow cytometer for cost-effective, high-throughput, and label-free analysis of natural water samples.

We report a deep learning-enabled field-portable and cost-effective imaging flow cytometer that automatically captures phase-contrast color images of the contents of a continuously flowing water sample at a throughput of 100 mL/h. The device is based on partially coherent lens-free holographic microscopy and acquires the diffraction patterns of flowing micro-objects inside a microfluidic channel. These holographic diffraction patterns are reconstructed in real time using a deep learning-based phase-recovery and image-reconstruction method to produce a color image of each micro-object without the use of external labeling. Motion blur is eliminated by simultaneously illuminating the sample with red, green, and blue light-emitting diodes that are pulsed. Operated by a laptop computer, this portable device measures 15.5 cm × 15 cm × 12.5 cm, weighs 1 kg, and compared to standard imaging flow cytometers, it provides extreme reductions of cost, size and weight while also providing a high volumetric throughput over a large object size range. We demonstrated the capabilities of this device by measuring ocean samples at the Los Angeles coastline and obtaining images of its micro- and nanoplankton composition. Furthermore, we measured the concentration of a potentially toxic alga (Pseudo-nitzschia) in six public beaches in Los Angeles and achieved good agreement with measurements conducted by the California Department of Public Health. The cost-effectiveness, compactness, and simplicity of this computational platform might lead to the creation of a network of imaging flow cytometers for large-scale and continuous monitoring of the ocean microbiome, including its plankton composition.

Wearable and Implantable Sensors for Biomedical Applications.

Mobile health technologies offer great promise for reducing healthcare costs and improving patient care. Wearable and implantable technologies are contributing to a transformation in the mobile health era in terms of improving healthcare and health outcomes and providing real-time guidance on improved health management and tracking. In this article, we review the biomedical applications of wearable and implantable medical devices and sensors, ranging from monitoring to prevention of diseases, as well as the materials used in the fabrication of these devices and the standards for wireless medical devices and mobile applications. We conclude by discussing some of the technical challenges in wearable and implantable technology and possible solutions for overcoming these difficulties.

Quantification of plant chlorophyll content using Google Glass.

Measuring plant chlorophyll concentration is a well-known and commonly used method in agriculture and environmental applications for monitoring plant health, which also correlates with many other plant parameters including, e.g., carotenoids, nitrogen, maximum green fluorescence, etc. Direct chlorophyll measurement using chemical extraction is destructive, complex and time-consuming, which has led to the development of mobile optical readers, providing non-destructive but at the same time relatively expensive tools for evaluation of plant chlorophyll levels. Here we demonstrate accurate measurement of chlorophyll concentration in plant leaves using Google Glass and a custom-developed software application together with a cost-effective leaf holder and multi-spectral illuminator device. Two images, taken using Google Glass, of a leaf placed in our portable illuminator device under red and white (i.e., broadband) light-emitting-diode (LED) illumination are uploaded to our servers for remote digital processing and chlorophyll quantification, with results returned to the user in less than 10 seconds. Intensity measurements extracted from the uploaded images are mapped against gold-standard colorimetric measurements made through a commercially available reader to generate calibration curves for plant leaf chlorophyll concentration. Using five plant species to calibrate our system, we demonstrate that our approach can accurately and rapidly estimate chlorophyll concentration of fifteen different plant species under both indoor and outdoor lighting conditions. This Google Glass based chlorophyll measurement platform can display the results in spatiotemporal and tabular forms and would be highly useful for monitoring of plant health in environmental and agriculture related applications, including e.g., urban plant monitoring, indirect measurements of the effects of climate change, and as an early indicator for water, soil, and air quality degradation.

Rapid imaging, detection and quantification of Giardia lamblia cysts using mobile-phone based fluorescent microscopy and machine learning.

Rapid and sensitive detection of waterborne pathogens in drinkable and recreational water sources is crucial for treating and preventing the spread of water related diseases, especially in resource-limited settings. Here we present a field-portable and cost-effective platform for detection and quantification of Giardia lamblia cysts, one of the most common waterborne parasites, which has a thick cell wall that makes it resistant to most water disinfection techniques including chlorination. The platform consists of a smartphone coupled with an opto-mechanical attachment weighing ~205 g, which utilizes a hand-held fluorescence microscope design aligned with the camera unit of the smartphone to image custom-designed disposable water sample cassettes. Each sample cassette is composed of absorbent pads and mechanical filter membranes; a membrane with 8 µm pore size is used as a porous spacing layer to prevent the backflow of particles to the upper membrane, while the top membrane with 5 µm pore size is used to capture the individual Giardia cysts that are fluorescently labeled. A fluorescence image of the filter surface (field-of-view: ~0.8 cm(2)) is captured and wirelessly transmitted via the mobile-phone to our servers for rapid processing using a machine learning algorithm that is trained on statistical features of Giardia cysts to automatically detect and count the cysts captured on the membrane. The results are then transmitted back to the mobile-phone in less than 2 minutes and are displayed through a smart application running on the phone. This mobile platform, along with our custom-developed sample preparation protocol, enables analysis of large volumes of water (e.g., 10-20 mL) for automated detection and enumeration of Giardia cysts in ~1 hour, including all the steps of sample preparation and analysis. We evaluated the performance of this approach using flow-cytometer-enumerated Giardia-contaminated water samples, demonstrating an average cyst capture efficiency of ~79% on our filter membrane along with a machine learning based cyst counting sensitivity of ~84%, yielding a limit-of-detection of ~12 cysts per 10 mL. Providing rapid detection and quantification of microorganisms, this field-portable imaging and sensing platform running on a mobile-phone could be useful for water quality monitoring in field and resource-limited settings.