Edible Matrix Code with Photogenic Silk Proteins.

Counterfeit medicines are a healthcare security problem, posing not only a direct threat to patient safety and public health but also causing heavy economic losses. Current anticounterfeiting methods are limited due to the toxicity of the constituent materials and the focus of secondary packaging level protections. We introduce an edible, imperceptible, and scalable matrix code of information representation and data storage for pharmaceutical products. This matrix code is digestible as it is composed of silk fibroin genetically encoded with fluorescent proteins produced by ecofriendly, sustainable silkworm farming. Three distinct fluorescence emission colors are incorporated into a multidimensional parameter space with a variable encoding capacity in a format of matrix arrays. This code is smartphone-readable to extract a digitized security key augmented by a deep neural network for overcoming fabrication imperfections and a cryptographic hash function for enhanced security. The biocompatibility, photostability, thermal stability, long-term reliability, and low bit error ratio of the code support the immediate feasibility for dosage-level anticounterfeit measures and authentication features. The edible code affixed to each medicine can serve as serialization, track and trace, and authentication at the dosage level, empowering every patient to play a role in combating illicit pharmaceuticals.

Ultrasonic assessment of osseointegration phenomena at the bone-implant interface using convolutional neural network.

Although endosseous implants are widely used in the clinic, failures still occur and their clinical performance depends on the quality of osseointegration phenomena at the bone-implant interface (BII), which are given by bone ingrowth around the BII. The difficulties in ensuring clinical reliability come from the complex nature of this interphase related to the implant surface roughness and the presence of a soft tissue layer (non-mineralized bone tissue) at the BII. The aim of the present study is to develop a method to assess the soft tissue thickness at the BII based on the analysis of its ultrasonic response using a simulation based-convolution neural network (CNN). A large-annotated dataset was constructed using a two-dimensional finite element model in the frequency domain considering a sinusoidal description of the BII. The proposed network was trained by the synthesized ultrasound responses and was validated by a separate dataset from the training process. The linear correlation between actual and estimated soft tissue thickness shows excellent R2 values equal to 99.52% and 99.65% and a narrow limit of agreement corresponding to [ -2.56, 4.32 µm] and [ -15.75, 30.35 µm] of microscopic and macroscopic roughness, respectively, supporting the reliability of the proposed assessment of osseointegration phenomena.

Compressive recovery of smartphone RGB spectral sensitivity functions.

Spectral response (or sensitivity) functions of a three-color image sensor (or trichromatic camera) allow a mapping from spectral stimuli to RGB color values. Like biological photosensors, digital RGB spectral responses are device dependent and significantly vary from model to model. Thus, the information on the RGB spectral response functions of a specific device is vital in a variety of computer vision as well as mobile health (mHealth) applications. Theoretically, spectral response functions can directly be measured with sophisticated calibration equipment in a specialized laboratory setting, which is not easily accessible for most application developers. As a result, several mathematical methods have been proposed relying on standard color references. Typical optimization frameworks with constraints are often complicated, requiring a large number of colors. We report a compressive sensing framework in the frequency domain for accurately predicting RGB spectral response functions only with several primary colors. Using a scientific camera, we first validate the estimation method with direct spectral sensitivity measurements and ensure that the root mean square errors between the ground truth and recovered RGB spectral response functions are negligible. We further recover the RGB spectral response functions of smartphones and validate with an expanded color checker reference. We expect that this simple yet reliable estimation method of RGB spectral sensitivity can easily be applied for color calibration and standardization in machine vision, hyperspectral filters, and mHealth applications that capitalize on the built-in cameras of smartphones.

A Pearl Spectrometer.

Information recovery from incomplete measurements, typically performed by a numerical means, is beneficial in a variety of classical and quantum signal processing. Random and sparse sampling with nanophotonic and light scattering approaches has received attention to overcome the hardware limitations of conventional spectrometers and hyperspectral imagers but requires high-precision nanofabrications and bulky media. We report a simple spectral information processing scheme in which light transport through an Anderson-localized medium serves as an entropy source for compressive sampling directly in the frequency domain. As implied by the "lustrous" reflection originating from the exquisite multilayered nanostructures, a pearl (or mother-of-pearl) allows us to exploit the spatial and spectral intensity fluctuations originating from strong light localization for extracting salient spectral information with a compact and thin form factor. Pearl-inspired light localization in low-dimensional structures can offer an alternative of spectral information processing by hybridizing digital and physical properties at a material level.

Effect of the static compressive load on vibration propagation in multistory buildings and resulting heavyweight floor impact sounds.

Experiments were performed to identify the mechanism of heavyweight floor impact sound transmission through floors in a high-rise apartment building. Vibration and sound levels on each floor of the multistory building were measured. The vibration generated at a given floor was transferred to multiple adjacent floors with decreasing amplitudes proportional to the distance from the excited floor. This vibration transfer introduced significant sound transmissions. The structural static load varied depending on the floor location due to differences in the weight of the structure above the floor, especially for wall construction buildings. The static load at the wall of the bottom floor was the largest among the different floors. The influence of this static load on the impact sound generation was investigated through tests in the actual building and the scale model, respectively. The results were numerically analyzed using the spectral element method. With the increasing static load, the resonance frequencies of the floor increased due to the change in the vibration modes of the structure. The modulated sound generation from the floor vibrations transmitted to multiple layers with larger magnitudes due to this static load.