Artificial Biomimetic Electrochemical Assemblies

Rapid, selective, and cost-effective detection and determination of clinically relevant biomolecule analytes for a better understanding of biological and physiological functions are becoming increasingly prominent. In this regard, biosensors represent a powerful tool to meet these requirements. Recent decades have seen biosensors gaining popularity due to their ability to design sensor platforms that are selective to determine target analytes. Naturally generated receptor units have a high affinity for their targets, which provides the selectivity of a device. However, such receptors are subject to instability under harsh environmental conditions and have consequently low durability. By applying principles of supramolecular chemistry, molecularly imprinted polymers (MIPs) can successfully replace natural receptors to circumvent these shortcomings. This review summarizes the recent achievements and analytical applications of electrosynthesized MIPs, in particular, for the detection of protein-based biomarkers. The scope of this review also includes the background behind electrochemical readouts and the origin of the gate effect in MIP-based biosensors.

Livestock Informatics Toolkit: A Case Study in Visually Characterizing Complex Behavioral Patterns across Multiple Sensor Platforms, Using Novel Unsupervised Machine Learning and Information Theoretic Approaches

Large and densely sampled sensor datasets can contain a range of complex stochastic structures that are difficult to accommodate in conventional linear models. This can confound attempts to build a more complete picture of an animal’s behavior by aggregating information across multiple asynchronous sensor platforms. The Livestock Informatics Toolkit (LIT) has been developed in R to better facilitate knowledge discovery of complex behavioral patterns across Precision Livestock Farming (PLF) data streams using novel unsupervised machine learning and information theoretic approaches. The utility of this analytical pipeline is demonstrated using data from a 6-month feed trial conducted on a closed herd of 185 mix-parity organic dairy cows. Insights into the tradeoffs between behaviors in time budgets acquired from ear tag accelerometer records were improved by augmenting conventional hierarchical clustering techniques with a novel simulation-based approach designed to mimic the complex error structures of sensor data. These simulations were then repurposed to compress the information in this data stream into robust empirically-determined encodings using a novel pruning algorithm. Nonparametric and semiparametric tests using mutual and pointwise information subsequently revealed complex nonlinear associations between encodings of overall time budgets and the order that cows entered the parlor to be milked.

Peptide-Based Capture of Chikungunya Virus E2 Protein Using Porous Silicon Biosensor

The detection of pathogens presents specific challenges in ensuring that biosensors remain operable despite exposure to elevated temperatures or other extreme conditions. The most vulnerable component of a biosensor is typically the bioreceptor. Accordingly, the robustness of peptides as bioreceptors offers improved stability and reliability toward harsh environments compared to monoclonal antibodies that may lose their ability to bind target molecules after such exposures. Here, we demonstrate peptide-based capture of the Chikungunya virus E2 protein in a porous silicon microcavity biosensor at room temperature and after exposure of the peptide-functionalized biosensor to high temperature. Contact angle measurements, attenuated total reflectance—Fourier transform infrared spectra, and optical reflectance measurements confirm peptide functionalization and selective E2 protein capture. This work opens the door for other pathogenic biomarker detection using peptide-based capture agents on porous silicon and other surface-based sensor platforms.

Label-Free SARS-CoV-2 Detection on Flexible Substrates

One of the most important strategies for mitigation and managing pandemics is widespread, rapid and inexpensive testing and isolation of infected patients. In this study, we demonstrate large area, label-free, and rapid testing sensor platforms fabricated on both rigid and flexible substrates for fast and accurate detection of SARS-CoV-2. SERS enhancing metal insulator metal (MIM) nanostructures are modeled using finite element simulations and then fabricated using nanoimprint lithography (NIL) and transfer printing. The SERS signal of various viral samples, including spiked saliva, was analyzed using machine learning classifiers. We observe that our approach can obtain the test results typically within 25 minutes with a detection accuracy of at least 83% for the viral samples. We envision that this approach which features large area nanopatterning, fabrication in both rigid and flexible formats for wearables, SERS spectroscopy and machine learning can enable new types of rapid, label-free biosensors for screening pathogens and managing current and future pandemics.

Emerging Sensor Platforms Allow for Seagrass Extent Mapping in a Turbid Estuary and from the Meadow to Ecosystem Scale

Seagrass meadows are globally important habitats, protecting shorelines, providing nursery areas for fish, and sequestering carbon. However, both anthropogenic and natural environmental stressors have led to a worldwide reduction seagrass habitats. For purposes of management and restoration, it is essential to produce accurate maps of seagrass meadows over a variety of spatial scales, resolutions, and at temporal frequencies ranging from months to years. Satellite remote sensing has been successfully employed to produce maps of seagrass in the past, but turbid waters and difficulty in obtaining low-tide scenes pose persistent challenges. This study builds on an increased availability of affordable high temporal frequency imaging platforms, using seasonal unmanned aerial vehicle (UAV) surveys of seagrass extent at the meadow scale, to inform machine learning classifications of satellite imagery of a 40 km2 bay. We find that object-based image analysis is suitable to detect seasonal trends in seagrass extent from UAV imagery and find that trends vary between individual meadows at our study site Bahía de San Quintín, Baja California, México, during our study period in 2019. We further suggest that compositing multiple satellite imagery classifications into a seagrass probability map allows for an estimation of seagrass extent in turbid waters and report that in 2019, seagrass covered 2324 ha of Bahía de San Quintín, indicating a recovery from losses reported for previous decades.

Real-World Evidence for a Smartwatch-Based Parkinson’s Motor Assessment App for Patients Undergoing Therapy Changes

<b><i>Introduction:</i></b> Parkinson’s disease (PD) is poorly quantified by patients outside the clinic, and paper diaries have problems with subjective descriptions and bias. Wearable sensor platforms; however, can accurately quantify symptoms such as tremor, dyskinesia, and bradykinesia. Commercially available smartwatches are equipped with accelerometers and gyroscopes that can measure motion for objective evaluation. We sought to evaluate the clinical utility of a prescription smartwatch-based monitoring system for PD utilizing periodic task-based motor assessment. <b><i>Methods:</i></b> Sixteen patients with PD used a smartphone- and smartwatch-based monitoring system to objectively assess motor symptoms for 1 week prior to instituting a doctor recommended change in therapy and for 4 weeks after the change. After 5 weeks the participants returned to the clinic to discuss their results with their doctor, who made therapy recommendations based on the reports and his clinical judgment. Symptom scores were synchronized with the medication diary and the temporal effects of therapy on weekly and hourly timescales were calculated. <b><i>Results:</i></b> Thirteen participants successfully completed the study and averaged 4.9 assessments per day for 3 days per week during the study. The doctor instructed 8 participants to continue their new regimens and 5 to revert to their previous regimens. The smartwatch-based assessments successfully captured intraday fluctuations and short- and long-term responses to therapies, including detecting significant improvements (<i>p</i> &#x3c; 0.05) in at least one symptom in 7 participants. <b><i>Conclusions:</i></b> The smartwatch-based app successfully captured temporal trends in symptom scores following application of new therapy on hourly, daily, and weekly timescales. These results suggest that validated smartwatch-based PD monitoring can provide clinically relevant information and may reduce the need for traditional office visits for therapy adjustment.

Evolutionary Multi-Objective One-Shot Filter Pruning for Designing Lightweight Convolutional Neural Network

Deep neural networks have achieved significant development and wide applications for their amazing performance. However, their complex structure, high computation and storage resource limit their applications in mobile or embedding devices such as sensor platforms. Neural network pruning is an efficient way to design a lightweight model from a well-trained complex deep neural network. In this paper, we propose an evolutionary multi-objective one-shot filter pruning method for designing a lightweight convolutional neural network. Firstly, unlike some famous iterative pruning methods, a one-shot pruning framework only needs to perform filter pruning and model fine-tuning once. Moreover, we built a constraint multi-objective filter pruning problem in which two objectives represent the filter pruning ratio and the accuracy of the pruned convolutional neural network, respectively. A non-dominated sorting-based evolutionary multi-objective algorithm was used to solve the filter pruning problem, and it provides a set of Pareto solutions which consists of a series of different trade-off pruned models. Finally, some models are uniformly selected from the set of Pareto solutions to be fine-tuned as the output of our method. The effectiveness of our method was demonstrated in experimental studies on four designed models, LeNet and AlexNet. Our method can prune over 85%, 82%, 75%, 65%, 91% and 68% filters with little accuracy loss on four designed models, LeNet and AlexNet, respectively.