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Heat shock proteins (HSPs) play a pivotal role as molecular chaperones against unfavorable conditions. Although HSPs are of great importance, their computational identification remains a significant challenge. Previous studies have two major limitations. First, they relied heavily on amino acid composition features, which inevitably limited their prediction performance. Second, their prediction performance was overestimated because of the independent two-stage evaluations and train-test data redundancy. To overcome these limitations, we introduce two novel deep learning algorithms: (1) time-efficient DeepHSP and (2) high-performance DeeperHSP. We propose a convolutional neural network (CNN)-based DeepHSP that classifies both non-HSPs and six HSP families simultaneously. It outperforms state-of-the-art algorithms, despite taking 14-15 times less time for both training and inference. We further improve the performance of DeepHSP by taking advantage of protein transfer learning. While DeepHSP is trained on raw protein sequences, DeeperHSP is trained on top of pre-trained protein representations. Therefore, DeeperHSP remarkably outperforms state-of-the-art algorithms increasing F1 scores in both cross-validation and independent test experiments by 20% and 10%, respectively. We envision that the proposed algorithms can provide a proteome-wide prediction of HSPs and help in various downstream analyses for pathology and clinical research., Competing Interests: The authors have declared that no competing interests exist.

Monitoring blood sugar level is highly important for managing diabetes and preventing complications. Herein, we describe a paper-based portable assay system for highly accurate monitoring of glucose levels in tears. The system combines enzyme reactions, the perturbation of fluorescence properties, automated image analysis, and pattern recognition powered by a machine-learning algorithms in a single assay without extra manual steps. A small size (20 × 20 mm) assay system was prepared simply by printing black wax crosswise on cellulose paper, melting the wax with heat, and depositing four different fluorescent compounds and glucose oxidase at the test zones. Applying a small drop of sample in the center zone of the system evenly distributes the samples to the test zone via the paper capillary process and eventually induces a generation of unique fluorescence pattern that is specific to glucose. Two models are used to analyze image data collected from the fluorescent compounds array to make accurate estimations on glucose concentration: a random forest model that discretely classifies the concentration with respect to the training data granularity and an SVM regression model that estimates glucose concentration at interpolated finer granularity. These models are combined with feature engineering techniques for efficient sensor data processing on resource limited mobile platforms. As a result, using the random forest approach, glucose concentration in artificial tears can be accurately classified with 96.7% accuracy (0.2 mM intervals) (MSE of 0.060 mM) and the SVM regression model results in an MSE of 0.026 mM. • A fluorescent compounds array system enables noninvasive glucose monitoring. • Photophysical properties of the compounds are altered by an enzyme reaction. • Smartphone-based system enables automated image analysis and pattern recognition. • We achieved 96.7% detection accuracy in two steps using the system. [ABSTRACT FROM AUTHOR]

There is growing research interest from many scientific, healthcare, and industrial applications toward the development of high-precision optical pH sensors that cover a broad pH range. Despite enthusiastic endeavors, however, it remains challenging to develop cost-effective, high-precision, and broadband working paper-strip-type optical pH measurement systems, particularly for on-site or in-the-field pH sensing applications. We develop a fluorescent array based on a KIz system for accurate pH level classification. Based on the indolizine fluorescent core skeleton, a library of 30 different pH-responsive fluorescent probes is rationally designed and efficiently synthesized. Spotting the compounds in a checkered pattern (5 × 6) allows for the development of a disposable compound array on wax-printed cellulose paper. Compounds sharing a single chemical core skeleton result in the interrogation of all the components of a system with a single excitation light, resulting in a simple system design for pH classification. Furthermore, we design a 3D-printed enclosure to capture the fluorescence pattern changes of the array by using an intelligent, smartphone-based, handheld pH detection system. Specifically, by exploiting a random forest-based machine learning algorithm on a smartphone, we can effectively analyze the fluorescence pattern changes. Our results suggest that our proposed system can classify pH levels in fine-grain (0.2 pH) units. • Synthesizing of 30 different fluorescent probes containing pH responding units based on the indolizine core skeleton. • Resulting compounds respond to pH with different photophysical property changes. • Exposure of the fluorescent compounds array with different pH generated distinct fluorescent pattern changes. • Machine learning algorithm allows for fluorescent-image based high-precision pH classification in broad pH ranges. [ABSTRACT FROM AUTHOR]

Improvements in small sized sensors allow the easy detection of the presence of Volatile Organic Compounds (VOCs) in the air using easy-to-deploy Internet of Things (IoT) devices. However, classifying what VOC exists in the environment still remains as a complex task. Knowing what VOCs are in the air can help us remove the main cause that vents VOC materials as a way to maintain clean air quality. In this work, we present VOCkit , an IoT sensor kit for non-chemical experts to easily detect and classify different types of VOCs. VOCkit combines miniature chemically-designed fluorometric sensors for recognizing VOCs with an embedded imaging system for classification. Exposing the fluorometric sensors with various VOCs, result in the photophysical property change of fluorescent compounds, which composes the sensors, and the synergistic combination of the changes create unique individual fluorescent color patterns respectively to the VOC material. The fluorescent color change pattern is captured using an embedded camera and the images are processed with machine learning algorithms on the embedded platform for VOC classification. Using 500 fluorometric sensor images collected for five different commonly contactable VOCs, we show the feasibility of VOC classification on small-sized IoT devices. For the VOC types of our interest, our results show a classification accuracy of 97%, implying the potential applicability of VOCkit for real-world usage. [ABSTRACT FROM AUTHOR]

Based on indolizine fluorescent core skeleton, a library of 75 different fluorescent derivatives were synthesized. Machine-learning algorithm allowed differentiation of 35 different volatile organic compounds via analysis of fluorescent pattern changes of fluorescent compound array, using a smartphone-based handheld detection system. Classification accuracy was 97 %. • The library of 75 different fluorescent KIz derivatives were designed and synthesized. • KIz core skeleton have the full-color-tunable solid state fluorescence property. • Fluorescent pattern changes of KIz array were specific to target chemicals adsorbed on that paper. • Powered by the custom-developed machine-learning algorithm, we achieved the detection accuracy of 97%. Multiplexed analysis allows simultaneous measurements of multiple targets, improving the detection sensitivity and accuracy. However, highly multiplexed analysis has been challenging for point-of-care (POC) sensing, which requires a simple, portable, robust, and affordable detection system. In this work, we developed paper-based POC sensing arrays consisting of kaleidoscopic fluorescent compounds. Using an indolizine structure as a fluorescent core skeleton, named Kaleidolizine (KIz), a library of 75 different fluorescent KIz derivatives were designed and synthesized. These KIz derivatives are simultaneously excited by a single ultraviolet (UV) light source and emit diverse fluorescence colors and intensities. For multiplexed POC sensing system, fluorescent compounds array on cellulose paper was prepared and the pattern of fluorescence changes of KIz on array were specific to target chemicals adsorbed on that paper. Furthermore, we developed a machine-learning algorithm for automated, rapid analysis of color and intensity changes of individual sensing arrays. We showed that the paper sensor arrays could differentiate 35 different volatile organic compounds using a smartphone-based handheld detection system. Powered by the custom-developed machine-learning algorithm, we achieved the detection accuracy of 97 % in the VOC detection. The highly multiplexed paper sensor could have favorable applications for monitoring a broad-range of environmental toxins, heavy metals, explosives, pathogens. [ABSTRACT FROM AUTHOR]