Due to the lack of specific early detection methods for pancreatic cancer, it usually goes undetected until it is advanced. By employing paper-based electrodes (PPE), herein we for the first time developed a disposable low-cost paper-based immunosensor for rapid early quantitative detection of pancreatic cancer with a new biomarker, pseudopodium-enriched atypical kinase one, SGK269 (PEAK1). The immunosensor was constructed by fabricating PPEs immobilized with the versatile nanomaterial graphene oxide for the incorporation of antibodies to form an immunosensing platform, without the need of complicated surface modification. After it was confirmed that the PPEs exhibited excellent electrochemical properties, a sandwich-type electrochemical immunosensor was subsequently constructed by employing graphene oxide layers immobilized with anti-PEAK1, and the antibody conjugated with gold nanoparticles (AuNPs-tagged-Anti PEAK1). Further, spectral and surface characteristic studies confirmed the formation of the immunosensing platform. The immunosensor for PEAK1 exhibited a wide linear range between 10 pg mL-1 and 106 pg mL-1 with a low limit of detection (LOD) of 10 pg mL-1. The obtained results point towards rapid, sensitive, and specific early diagnosis of pancreatic cancer at the point of care and other low-resource settings.Quantitative analysis using thin-layer chromatography coupled in tandem with surface-enhanced Raman scattering (TLC-SERS) still remains a grand challenge due to many uncontrollable variations during the TLC developing process and the random nature of the SERS substrates. Traditional chemometric methods solve this problem by sampling multiple SERS spectra in the sensing spot and then conducting statistical analysis of the SERS signals to mitigate the variation of quantitative analysis, while still ignoring the spatial distribution of the target species and the correlation among the multiple sampling points. In this paper, we proposed for the first time a parallel feature extraction and fusion method based on quaternion signal processing techniques, which can enable quantitative analysis using recently established TLC-SERS techniques. By marking three deterministic sampling points, we recorded spatially correlated SERS spectra to constitute an integral representation model of triple-spectra by a pure quaternion matrix. Quaternion principal component analysis (QPCA) was utilized for features extraction and followed by feature crossing among the quaternion principal components to obtain final fusion spectral feature vectors. Support vector regression (SVR) was then used to establish the quantitative model of melamine-contaminated milk samples with seven concentrations (1ppm to 250ppm). Compared with traditional TLC-SERS analysis methods, QPCA method significantly improved the accuracy of quantification by reaching only 7% and 2% quantization errors at 20 and 105 ppm concentration. Validation testing based on reasonable amount of statistic measurement results showed consistently smaller measurement errors and variance, which proved the effectiveness of QPCA method for TLC-SERS based quantitative sensing applications.Hypochlorous acid (HOCl) as one of the most important reactive oxygen species in the organism, its role is more and more recognized. In fact, in recent years, various HOCl fluorescent probes have been developed unprecedentively based on various mechanisms. However, because most of the mechanisms are based on the oxidation characteristics of HOCl, the excellent detection performance of probes depends on the activation ability of some functional groups to reaction sites. The C[bond, double bond]C bond in the probe is often oxidized by HOCl to realize HOCl detection. However, due to the break of conjugated structure, the probe often present as a quenchable or turning on fluorescence emission. In this work, malononitrile was introduced as the "double-edged sword" of passivation-activation when in HOCl fluorescent probe was designed. Passivation-activation regulated two ICT (Intermolecular Charge Transfer, ICT) processes to ratiometric fluorescent detection for HOCl. Highly sensitive and accurate detection realized efficient application in biological imaging.With the rapid development of image acquisition and storage, multiple images per class are commonly available for computer vision tasks (e.g., face recognition, object detection, medical imaging, etc.). Recently, the recurrent neural network (RNN) has been widely integrated with convolutional neural networks (CNN) to perform image classification on ordered (sequential) data. In this paper, by permutating multiple images as multiple dummy orders, we generalize the ordered "RNN+CNN" design (longitudinal) to a novel unordered fashion, called Multi-path x-D Recurrent Neural Network (MxDRNN) for image classification. To the best of our knowledge, few (if any) existing studies have deployed the RNN framework to unordered intra-class images to leverage classification performance. Specifically, multiple learning paths are introduced in the MxDRNN to extract discriminative features by permutating input dummy orders. Eight datasets from five different fields (MNIST, 3D-MNIST, CIFAR, VGGFace2, and lung screening computed tomography) are included to evaluate the performance of our method.  https://www.selleckchem.com/products/actinomycin-d.html  The proposed MxDRNN improves the baseline performance by a large margin across the different application fields (e.g., accuracy from 46.40% to 76.54% in VGGFace2 test pose set, AUC from 0.7418 to 0.8162 in NLST lung dataset). Additionally, empirical experiments show the MxDRNN is more robust to category-irrelevant attributes (e.g., expression, pose in face images), which may introduce difficulties for image classification and algorithm generalizability. The code is publicly available.Deep learning has achieved impressive performance across a variety of tasks, including medical image processing. However, recent research has shown that deep neural networks are susceptible to small adversarial perturbations in the image. We study the impact of such adversarial perturbations in medical image processing where the goal is to predict an individual's age based on a 3D MRI brain image. We consider two models a conventional deep neural network, and a hybrid deep learning model which additionally uses features informed by anatomical context. We find that we can introduce significant errors in predicted age by adding imperceptible noise to an image, can accomplish this even for large batches of images using a single perturbation, and that the hybrid model is much more robust to adversarial perturbations than the conventional deep neural network. Our work highlights limitations of current deep learning techniques in clinical applications, and suggests a path forward.