Flexible, Fully Printable, and Inexpensive Paper-Based Chipless Arabic Alphabet-Based RFID Tags

This work presents the design and analysis of newly developed reconfigurable, flexible, inexpensive, optically-controlled, and fully printable chipless Arabic alphabet-based radio frequency identification (RFID) tags. The etching of the metallic copper tag strip is performed on a flexible simple thin paper substrate (ϵr = 2.31) backed by a metallic ground plane. The analysis of investigated tags is performed in CST MWS in the frequency range of 1–12 GHz for the determination of the unique signature resonance characteristics of each tag in terms of its back-scattered horizontal and vertical mono-static radar cross section (RCS). The analysis reflects that each tag has its own unique electromagnetic signature (EMS) due to the changing current distribution of metallic resonator. This EMS of each tag could be used for the robust detection and recognition of all realized 28 Arabic alphabet tags. The study also discusses, for the first time, the effect of the change in font type and size of realized tags on their EMS. The robustness and reliability of the obtained EMS of letter tags is confirmed by comparing the RCS results for selective letter tags using FDTD and MoM numerical methods, which shows very good agreement. The proposed tags could be used for smart internet of things (IoT) and product marketing applications.

Fourier spotting: a novel setup for single-color reflectometry

Single-color reflectrometry is a sensitive and robust detection method in optical biosensor applications, for example for bioanalysis. It is based on the interference of reflected monochromatic radiation and is label free. We present a novel setup for single-color reflectometry based on the patented technology of Berner et al. from 2016. Tilting areas of micro-mirrors allow us to encode the optical reflection signal of an analyte and reference channel into a particular carrier frequency with the amplitude being proportional to the local reflection. Therefore, a single photodiode is sufficient to collect the signals from both channels simultaneously. A 180∘ phase shift in the tilt frequency of two calibrated micro-mirror areas leads to a superposition of the analyte and reference signal which enables an efficient reduction of the baseline offset and potential baseline offset drift. A performance test reveals that we are able to detect changes of the refractive index n down to Δn < 0.01 of saline solutions as regents. A further test validates the detection of heterogeneous binding interaction. This test compromises immobilized testosterone-bovine serum albumin on a three-dimensional layer of biopolymer as ligand and monoclonal anti-testosterone antibodies as analyte. Antibody/antigen binding induces a local growth of the biolayer and change in the refractive index, which is measured via the local change of the reflection. Reproducible measurements enable for the analysis of the binding kinetics by determining the affinity constant KA = 1.59 × 10− 7 M− 1. In summary, this work shows that the concept of differential Fourier spotting as novel setup for single-color reflectometry is suitable for reliable bioanalysis.

Generation of bright autobioluminescent bacteria by chromosomal integration of the improved lux operon ilux2

The bacterial bioluminescence system enables light production in living cells without an external luciferin. Due to its relatively low levels of light emission, many applications of bioluminescence imaging would benefit from an increase in brightness of this system. In this report a new approach of mutagenesis and screening of the involved proteins is described that is based on the identification of mutants with improved properties under rate-limiting reaction conditions. Multiple rounds of screening in Escherichia coli resulted in the operon ilux2 that contains 26 new mutations in the fatty acid reductase complex which provides the aldehyde substrate for the bioluminescence reaction. Chromosomal integration of ilux2 yielded an autonomously bioluminescent E. coli strain with 7-fold increased brightness compared to the previously described ilux operon. The ilux2 strain produces sufficient signal for the robust detection of individual cells and enables highly sensitive long-term imaging of bacterial propagation without a selection marker.

EYE-C: Eye-Contact Robust Detection and Analysis during Unconstrained Child-Therapist Interactions in the Clinical Setting of Autism Spectrum Disorders

The high level of heterogeneity in Autism Spectrum Disorder (ASD) and the lack of systematic measurements complicate predicting outcomes of early intervention and the identification of better-tailored treatment programs. Computational phenotyping may assist therapists in monitoring child behavior through quantitative measures and personalizing the intervention based on individual characteristics; still, real-world behavioral analysis is an ongoing challenge. For this purpose, we designed EYE-C, a system based on OpenPose and Gaze360 for fine-grained analysis of eye-contact episodes in unconstrained therapist-child interactions via a single video camera. The model was validated on video data varying in resolution and setting, achieving promising performance. We further tested EYE-C on a clinical sample of 62 preschoolers with ASD for spectrum stratification based on eye-contact features and age. By unsupervised clustering, three distinct sub-groups were identified, differentiated by eye-contact dynamics and a specific clinical phenotype. Overall, this study highlights the potential of Artificial Intelligence in categorizing atypical behavior and providing translational solutions that might assist clinical practice.

Multivariate statistical monitoring system for microbial population dynamics

Microbiomes in their natural environments vary dynamically with changing environmental conditions. The detection of these dynamic changes in microbial populations is critical for understanding the impact of environmental changes on the microbial community. Here, we propose a novel method to detect time-series changes in the microbiome, based on multivariate statistical process control. By focusing on the interspecies structures, this approach enables the robust detection of time-series changes in a microbiome composed of a large number of microbial species. Applying this approach to empirical human gut microbiome data, we accurately traced time-series changes in microbiota composition induced by a dietary intervention trial. This method was also excellent for tracking the recovery process after the intervention. Our approach can be useful for monitoring dynamic changes in complex microbial communities.