关键词： Structural health monitoring   Damage identification   Pattern recognition   Self-powered sensors   Binary data  

摘要： This study presents a novel strategy for localized damage identification in plate-like structures based on the binary data provided by self-powered wireless sensors that use a pulse switching communication architecture. The energy-aware pulse switching communication architecture uses single pulses instead of multi-bit packets for information delivery resulting in discrete binary data. A system employing such an energy-efficient technology requires dealing with power budgets for sensing and communication that leads to unique time delay constraints. This paper presents a new paradigm for localized damage detection using time-delayed and sometimes limited binary data. The binary data from the sensor nodes resembles an image, or pattern. Data analysis using pattern recognition (PR) framework incorporating PR methods and a conditional probability chain is thus presented. Numerous features extracted from cumulative acceleration on dynamically loaded plates are used to determine damage indication parameters. Performance of the proposed damage detection strategy was evaluated through finite element simulations for the case of a simply supported aluminum plate under distributed harmonic loading. Different damage states were considered to calibrate the damage detection algorithm, and an uncertainty analysis was performed by superposing random noise to the input vectors. Results show that the proposed PR framework is capable of identifying damage even in the presence of high noise levels. The findings demonstrate satisfactory performance of the localized damage detection strategy, and the applicability of PR to detect and localize damage in plate-like structures from the time-delayed binary data generated by self-powered sensors. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Peptides and proteins   Metabolism   Labeling   Nucleic acid structure   Receptors  

摘要： The peptidoglycan fragments y-D-glutamyl-meso-diaminopimelic acid (iE-DAP) and muramyl-dipeptide (MDP) are microbial-specific metabolites that activate intracellular pattern recognition receptors and stimulate immune signaling pathways. While extensive structure activity studies have demonstrated that these bacterial cell wall metabolites trigger NOD1-and NOD2-dependent signaling, their direct binding to these innate immune receptors or other proteins in mammalian cells has not been established. To characterize these fundamental microbial metabolite-host interactions, we synthesized a series of peptidoglycan metabolite photoaffinity reporters and evaluated their cross-linking to NOD1 and NOD2 in mammalian cells. We show that active iE-DAP and MDP photoaffinity reporters selectively cross-linked NOD1 and NOD2, respectively, and not their inactive mutants. We also discovered MDP reporter cross-linking to Arf GTPases, which interacted most prominently with GTP-bound Arf6 and coimmunoprecipitated with NOD2 upon MDP stimulation. Notably, MDP binding to NOD2 and Arf6 was abrogated with loss-of-function NOD2 mutants associated with Crohn's disease. Our studies demonstrate peptidoglycan metabolite photoaffinity reporters can capture their cognate immune receptors in cells and reveal unpredicted ligand-induced interactions with other cellular cofactors. These photoaffinity reporters should afford useful tools to discover and characterize other peptidoglycan metabolite-interacting proteins.

关键词： Anatomy   Biological tissue   Biology and life sciences   Connective tissue   Diagnostic medicine   Diagnostic radiology   Health care   Health care providers   Imaging techniques   Lesions   Medical doctors   Medical personnel   Medicine and health sciences   Muscles   Musculoskeletal system   Musculoskeletal system procedures   Orthopedic surgery   Pathology and laboratory medicine   People and places   Physicians   Population groupings   Professions   Radiologists   Radiology and imaging   Research and analysis methods   Research Article   Rotator cuff muscles   Shoulders   Signs and symptoms   Surgeons   Surgical and invasive medical procedures   Tendons   Ultrasound imaging  

摘要： The lifetime prevalence of shoulder pain is nearly 70% and is mostly attributable to subacromial disorders. A rotator cuff tear is the most severe form of subacromial disorders, and most occur in the supraspinatus. For clinical examination, shoulder ultrasound is recommended to detect supraspinatus tears. In this study, a computer-aided tear classification (CTC) system was developed to identify supraspinatus tears in ultrasound examinations and reduce inter-operator variability. The observed cases included 89 ultrasound images of supraspinatus tendinopathy and 102 of supraspinatus tear from 136 patients. For each case, intensity and texture features were extracted from the entire lesion and combined in a binary logistic regression classifier for lesion classification. The proposed CTC system achieved an accuracy rate of 92% (176/191) and an area under receiver operating characteristic curve (Az) of 0.9694. Based on its diagnostic performance, the CTC system has promise for clinical use.

摘要： We develop a single-color multiplexing strategy by the integration of high-resolution melting pattern recognition with loop-mediated isothermal amplification (LAMP). This strategy can identify multiple amplicons with a small DNA melting temperature (T-m) difference (similar to 0.2 degrees C) without the involvement of either multicolor labels or parallelized multiplexing, and it can sensitively detect LAMP amplicons with the initial DNA concentrations ranging from 10 to 10(8) copies.

关键词： Spiking neural network   STDP, AMPA and GABA current   Digit Recognition   Image coding  

摘要： The recent progress of low-power neuromorphic hardware provides exceptional conditions for applications where their focus is more on saving power. However, the design of spiking neural networks (SNN) to recognize real-world patterns on such hardware remains a major challenge ahead of the researchers. In this paper, SNN inspired by the model of local cortical population as a biological neuro-computing resource for digit recognition was presented. SNN was equipped with spike-based unsupervised weight optimization based on the dynamical behavior of the excitatory (AMPA) and inhibitory (GABA) synapses using Spike Timing Dependent Plasticity (STDP). This biologically plausible learning enables neurons to make decisions and learns the structure of the input examples. There are two main reasons why this structure is state of the art compared to previous works: learning process is compatible with many experimental observations on induction of long-term potentiation and long-term depression, image to signal mapping created an informative signal of the image based on sequences of prolate spheroidal wave functions (PSWFs). The proposed image mapping translates the pixels attributes to the frequency, phase, and amplitude of a sinusoidal signal. This mapping enables the SNN to generalize better to the realistic sized images and significantly decreases the size of the input layer. Cortical SNN compared to earlier related studies recognized MNIST digits more accurate and achieved 96.1% classification accuracy with unsupervised learning based on sparse spike activity. (C) 2018 Elsevier B.V. All rights reserved.

关键词： PATTERN perception   TIME series analysis   DATA modeling   INFORMATION visualization   MICROCLUSTERS  

摘要： BackgroundLife science research is moving quickly towards large-scale experimental designs that are comprised of multiple tissues, time points, and samples. Omic time-series experiments offer answers to three big questions: what collective patterns do most analytes follow, which analytes follow an identical pattern or synchronize across multiple cohorts, and how do biological functions evolve over time. Existing tools fall short of robustly answering and visualizing all three questions in a unified *** Heatmap offers time-series data visualization through a Master Panel page, and Combined page to answer each of the three time-series questions. It dissects the complex multi-omics time-series readouts into patterned clusters with associated biological functions. It allows users to identify a cascade of functional changes over a time variable. Inversely, Functional Heatmap can compare a pattern with specific biology respond to multiple experimental conditions. All analyses are interactive, searchable, and exportable in a form of heatmap, line-chart, or text, and the results are easy to share, maintain, and reproduce on the web *** Heatmap is an automated and interactive tool that enables pattern recognition in time-series multi-omics assays. It significantly reduces the manual labour of pattern discovery and comparison by transferring statistical models into visual clues. The new pattern recognition feature will help researchers identify hidden trends driven by functional changes using multi-tissues/conditions on a time-series fashion from omic assays.

关键词： Analytical methods   Chemical sensors   Pattern recognition   E-Nose   VOC identification  

摘要： Currently, the development of smart systems for recognition and sensing of volatile organic compounds (VOCs) in medical, agricultural, biometric, security and safety, applications is an emerging research area. This review presents an introduction to the field of VOC recognition by analytical methods and sensing by chemical sensors. The role of pattern recognition methods in the analysis of sensor array response is briefly discussed. Besides, the electronic nose (E-Nose) system (a bio-inspired prototype of the natural olfaction system by combining a chemical sensor array and pattern recognition methods) and its significance for VOCs recognition and sensing in different applications is explained. The study concludes current constraint and future prospects of VOC recognition and sensing in real-time applications.

关键词： aldehydes   excitation emission matrix   fluorescence   fluorogenic probes   pattern matching  

摘要： Biogenic carbonyls, especially aldehydes, have previously demonstrated their potential to serve as early diagnostic biomarkers for disease and injury that have not been fully realized owing, in part, to the lack of a rapid and simple point-of-care method for aldehyde identification. The ability to determine which carbonyl compound is elevated and not just the total aldehydic load may provide more disease- or injury-specific diagnostic information. Toward this end, a novel fluorophore is presented that is able to form a complex with biogenic carbonyls under catalyst-free conditions so as to give a fluorescent fingerprint of the resulting hydrazone. The successful identification of bound carbonyls was accomplished with a newly described algorithm that applied principal curvature analysis of excitation-emission matrices to reduce surface features to ellipse representations, followed by a pattern-matching routine. With this algorithm, carbonyls were identified over a range of concentrations, and mixture components were successfully parsed. Overall, the results presented lay the groundwork for novel implementations of chemometrics to low-cost, rapid, and simple-to-implement point-of-care diagnostics.

关键词： thin layer chromatography   System identification   Screening Method   Smartphone   Pattern recognition   LCN1 gene   image analysis  

摘要： Thin-layer chromatography (TLC) is commonly used as a screening method to verify the identity and quality of dried herbal medicinal plant material. While TLC is relatively simple, the method still requires technical experience and relies on the subjective classification of sample TLC profiles as "within-specifications" or "off-specifications." In this work, we report the development of an objective TLC-based system for the identification and quality assessment of herbal medicinal materials. Our proposed system is a miniaturized Pharmacopeia-based TLC method coupled with a smartphone app that allows for an objective interpretation of TLC profiles via multivariate image analysis and chemometric fingerprinting. An image of the TLC profile is captured using a smartphone camera interfaced with a 3D-printed photo-box, and the analysis is automated using a framework of pre-uploaded algorithms hosted on a cloud server. The TLC profile image is converted to an unfolded red, green, and blue (RGB) channel intensity profile, and classified as "within-specifications" or "off-specifications" using aggregated Soft Independent Modeling of Class Analogy (SIMCA) models. We present the application of our system to two herbal medicinal plants, Blumea balsamifera and Vitex negundo. The proposed system demonstrates 90.2% sensitivity and 86.2% specificity for B. balsamifera classification, and 81.4% sensitivity and 92.0% specificity for V. negundo classification when compared to the respective laboratory-based Pharmacopeia TLC protocols for the ability to distinguish authentic samples from non-authentic and degraded samples. The system developed in this work is a cost-effective, rapid method that can serve as a herbal material quality assessment tool in resource-limited settings.

关键词： Sensors   KRAS   p53   stochastic sensors  

摘要： Kirsten Rat Sarcoma (KRAS) and protein tumor suppressor (p53) are two biomarkers used to establish the correct diagnostic of cancer and also the personalized treatment. This paper proposed 2,6-bis((E)-2-(furan-2-yl)vinyl)-4-(4,6,8-trimethylazulen-1-yl)pyridine as nanostructured material for the design of stochastic sensors for fast recognition of p53 and KRAS in whole blood samples. The proposed stochastic sensors recognized p53 and KRAS in whole blood samples, with high sensitivities, at very low concentrations (fg/mL). The recovery tests performed proved that p53 and KRAS were determined in whole blood samples with average recoveries higher than 98.00% and RSD (%) values less than 0.20%, when they are in very small amounts. No sample treatment was required for whole blood samples collected from the patients. (C) 2019 The Electrochemical Society.