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Adeno-associated viruses (AAVs) are a leading vector for gene therapy, yet their clinical utility is limited by the lack of robust quality control methods to distinguish between empty (AAVempty), partially loaded (AAVpartial), and fully DNA loaded (AAVfull) capsids. Current analytical techniques provide partial insights but remain limited in sensitivity, throughput, or resolution. Here we present a multimodal plasmonic nanopore sensor that integrates optical trapping with electrical resistive-pulse sensing to characterize AAV9 capsids at the single-particle level in tens of μL sample volumes and fM range concentrations. As a model system, we employed AAV9 capsids not loaded with DNA, capsids loaded with a self-complementary 4.7 kbp DNA (AAVscDNA), and ones loaded with single-stranded 4.7 kbp DNA (AAVssDNA). Ground-truth validation was performed with analytical ultracentrifugation (AUC). Nanosensor data were acquired concurrently for optical step changes (occurring at AAV trapping and un-trapping) both in transmittance and reflectance geometries, and electrical nanopore resistive pulse signatures, making for a total of five data dimensions. The acquired data was then filtered and clustered by Gaussian mixture models (GMMs), accompanied by spectral clustering stability analysis, to successfully separate between AAV species based on their DNA load status (AAVempty, AAVpartial, AAVfull) and DNA load type (AAVscDNA versus AAVssDNA). The motivation for quantifying the AAVempty and AAVpartial population fractions is that they reduce treatment efficacy and increase immunogenicity. Likewise, the motivation to identify AAVscDNA population fractions is that these have much higher transfection rates. Importantly, the results showed that the nanosensor could differentiate between AAVscDNA and AAVssDNA despite their identical masses. In contrast, AUC could not differentiate between AAVscDNA and AAVssDNA. An equimolar mixture of AAVscDNA, AAVssDNA and AAVempty was also measured with the sensor, and the results showed the expected population fractions, supporting the capacity of the method to differentiate AAV load status in heterogeneous solutions. In addition, less common optical and electrical signal signatures were identified in the acquired data, which were attributed to debris, rapid entry re-entry to the optical trap, or weak optical trap exits, representing critical artifacts to recognize for correct interpretation of the data. Together, these findings establish plasmonic nanopore sensing as a promising platform for quantifying AAV DNA loading status and genome type with the potential to extend ultra-sensitive single-particle characterization beyond the capabilities of existing methods. 

As the size and complexity of high-performance computing (HPC) systems keep growing, scientists' ability to trust the data produced is paramount due to potential data corruption for various reasons, which may stay undetected. While employing machine learning-based anomaly detection techniques could relieve scientists of such concern, it is practically infeasible due to the need for labels for volumes of scientific datasets and the unwanted extra overhead associated. In this paper, we exploit spatial sparsity profiles exhibited in scientific datasets and propose an approach to detect anomalies effectively. Our method first extracts block-level sparse representations of original datasets in the transformed domain. Then it learns from the extracted sparse representations and builds the boundary threshold between normal and abnormal without relying on labeled data. Experiments using real-world scientific datasets show that the proposed approach requires 13% on average (less than 10% in most cases and as low as 0.3%) of the entire dataset to achieve competitive detection accuracy (70.74%-100.0%) as compared to two state-of-the-art unsupervised techniques. 

Advances in flexible and printable sensor technologies have made it possible to use posture classification for providing timely services in digital healthcare, especially for bedsores or decubitus ulcers. However, managing a large amount of sensor data and ensuring accurate predictions can be challenging. While lossy compressors can reduce data volume, it is still unclear whether this would lead to losing important information and affect downstream application performance. In this paper, we propose LCDNN (Lossy Compression using Deep Neural Network) to reduce the size of sensor data and evaluate the performance of posture classification models. Our sensors, placed under hospital beds, have a thickness of just 0.4mm and collect pressure data from 28 sensors (7 by 4) at an 8 Hz cycle, categorizing postures into 4 types from 5 patients. Our evaluation, which includes reduced datasets by LCDNN, demonstrates that the results are promising. 

Recent years have witnessed an upsurge of interest in lossy compression due to its potential to significantly reduce data volume with adequate exploitation of the spatiotemporal properties of IoT datasets. However, striking a balance between compression ratios and data fidelity is challenging, particularly when losing data fidelity impacts downstream data analytics noticeably. In this paper, we propose a lossy prediction model dealing with binary classification analytics tasks to minimize the impact of the error introduced due to lossy compression. We specifically focus on five classification algorithms for frost prediction in agricultural fields allowing preparation by the predictive advisories to provide helpful information for timely services. While our experimental evaluations reaffirm the nature of lossy compressions where allowing higher errors offers higher compression ratios, we also observe that the classification performance in terms of accuracy and F-1 score differs among all the algorithms we evaluated. Specifically, random forest is the best lossy prediction model for classifying frost. Lastly, we show the robustness of the lossy prediction model based on the data fidelity in prediction performance. 

As the scale and complexity of high-performance computing (HPC) systems keep growing, data compression techniques are often adopted to reduce the data volume and processing time. While lossy compression becomes preferable to a lossless one because of the potential benefit of generating a high compression ratio, it would lose its worth the effort without finding an optimal balance between volume reduction and information loss. Among many lossy compression techniques, transform-based lossy algorithms utilize spatial redundancy better. However, the transform-based lossy compressor has received relatively less attention because there is a lack of understanding of its compression performance on scientific data sets. The insight of this paper is that, in transform-based lossy compressors, quantifying dominant coefficients at the block level reveals the right balance, potentially impacting overall compression ratios. Motivated by this, we characterize three transformation-based lossy compression mechanisms with different information compaction methods using the statistical features that capture data characteristics. And then, we build several prediction models using the statistical features and the characteristics of dominant coefficients and evaluate the effectiveness of each model using six HPC datasets from three production-level simulations at scale. Our results demonstrate that the random forest classifier captures the behavior of dominant coefficients precisely, achieving nearly 99% of prediction accuracy. 

Reconfigurable modular robots can dynamically assemble/disassemble to accomplish the desired task better. Magnetic modular cubes are scalable modular subunits with embedded permanent magnets in a 3D-printed cubic body and can be wirelessly controlled by an external, uniform, timevarying magnetic field. This paper considers the problem of self-assembling these modules into desired 2D polyomino shapes using such magnetic fields. Although the applied magnetic field is the same for each magnetic modular cube, we use collisions with workspace boundaries to rearrange the cubes. We present a closed-loop control method for self-assembling the magnetic modular cubes into polyomino shapes, using computer vision-based feedback with re-planning. Experimental results demonstrate that the proposed closed-loop control improves the success rate of forming 2D user-specified polyominoes compared to an open-loop baseline. We also demonstrate the validity of the approach over changes in length scales, testing with both 10mm edge length cubes and 2.8mm edge length cubes. 

Alternating current (AC) modulation of command voltage applied across a Self-induced Back Action Actuated Nanopore Electrophoresis (SANE) sensor, a type of plasmonic nanopore sensor that we have developed previously, enables acquisition of new data types that could potentially enhance the characterization of nanoparticles (NPs) and single molecules. In particular, AC voltage frequency response provides insight into the charge and dielectric constant of analytes that are normally obfuscated using DC command voltages. We first analyzed Axopatch 200B data to map the frequency response of the empty SANE sensor in terms of phase shift and amplitude modulation, with and without plasmonic excitation. We then tested the frequency response of 20 nm diameter silica NPs and 20 nm gold NPs trapped optically, which made these particles hover over an underlying 25 nm nanopore at the center of the SANE sensor. By applying a DC command voltage with a superimposed AC frequency sweep while keeping the NPs optically trapped in the vicinity of the nanopores’s entrance, we have found that silica and gold NPs to have distinctly different electrical responses. This pilot work demonstrates the feasibility of performing AC measurements with a plasmonic nanopore, which encourages us to pursue more detailed characterization studies with NPs and single molecules in future work.