Detection of extremely low concentration waterborne pathogen using a multiplexing self-referencing SERS microfluidic biosensor

Citation: Wang, C., Madiyar, F., Yu, C. X., & Li, J. (2017). Detection of extremely low concentration waterborne pathogen using a multiplexing self-referencing SERS microfluidic biosensor. Journal of Biological Engineering, 11, 11. doi:10.1186/s13036-017-0051-xBackground: It is challenging to achieve ultrasensitive and selective detection of waterborne pathogens at extremely low levels (i.e., single cell/mL) using conventional methods. Even with molecular methods such as ELISA or PCR, multi-enrichment steps are needed which are labor and cost intensive. In this study, we incorporated nano-dielectrophoretic microfluidic device with Surface enhanced Raman scattering (SERS) technique to build a novel portable biosensor for easy detection and characterization of Escherichia coli O157:H7 at high sensitivity level (single cell/mL). Results: A multiplexing dual recognition SERS scheme was developed to achieve one-step target detection without the need to separate target-bound probes from unbound ones. With three different SERS-tagged molecular probes targeting different epitopes of the same pathogen being deployed simultaneously, detection of pathogen targets was achieved at single cell level with sub-species specificity that has not been reported before in single-step pathogen detection. Conclusion: The self-referencing protocol implements with a Nano-dielectrophoretic microfluidic device potentially can become an easy-to-use, field-deployable spectroscopic sensor for onsite detection of pathogenic microorganisms

Rapid-Response and Highly Sensitive Noncross-Linking Colorimetric Nitrite Sensor Using 4-Aminothiophenol Modified Gold Nanorods

A novel colorimetric nitrite ion sensor was developed utilizing 4-aminothiophenol (4-ATP) modified gold nanorods (GNR). In the presence of nitrite ions, the deamination reaction was induced by heating the 4-ATP modified GNR in ethanol solution, resulting in the reduction of the GNR surface charges, which led to aggregation of GNRs and a colorimetric response that was quantitatively correlated to the concentration of nitrite ions. This simple assay was rapid (≤10 min) and highly sensitive (\u3c1 ppm of nitrite), and it can be used for rapid monitoring of drinking water quality

Low Frequency Quasi-periodic Oscillation in MAXI J1820+070: Revealing distinct Compton and Reflection Contributions

X-ray low frequency quasi-periodic oscillations (LFQPOs) of black hole X-ray binaries, especially those type-C LFQPOs, are representative timing signals of black hole low/hard state and intermediate state, which has been suspected as to originate due to Lense-Thirring precession of the accretion flow. Here we report an analysis of one of the \emph{Insight}-HXMT observations of the black hole transient MAXI J1820$+$070 taken near the flux peak of its hard spectral state during which strong type-C LFQPOs were detected in all three instruments up to photon energies above 150 keV. We obtained and analyzed the short-timescale X-ray spectra corresponding to high- and low-intensity phases of the observed LFQPO waveform with a spectral model composed of Comptonization and disk reflection components. We found that the normalization of the spectral model is the primary parameter that varied between the low and high-intensity phases. The variation in the LFQPO flux at the hard X-ray band (> 100 keV) is from the Compton component alone, while the energy-dependent variation in the LFQPO flux at lower energies (< 30 keV) is mainly caused by the reflection component with a large reflection fraction in response to the incident Compton component. The observed X-ray LFQPOs thus should be understood as manifesting the original timing signals or beats in the hard Compton component, which gives rise to additional variability in softer energies due to disk reflection.Comment: 8 pages, 4 figures, accepted for publication in MNRA

Transport of Artificial Virus-like Nanocarriers (AVN) through intestinal monolayer via Microfold cells

Compared with subcutaneous or intramuscular routes for vaccination, vaccine delivery via gastrointestinal mucosa has tremendous potential as it is easy to administer and pain free. Robust immune responses can be triggered successfully once vaccine carried antigen reaches the mucosal associated lymphoid sites (e.g., Peyer’s patches). However, the absence of an efficient delivery method has always been an issue for successful oral vaccine development. In our study, inspired by mammalian orthoreovirus (MRV) transport into gut mucosal lymphoid tissue via Microfold cells (M cells), artificial virus-like nanocarriers (AVN), consisting of gold nanocages functionalized with the 1 protein from mammalian reovirus (MRV), were tested as an effective oral vaccine delivery vehicle targeting M cells. AVN was shown to have a significantly higher transport compared to other experimental groups across mouse organoid monolayers containing M cells. These findings suggest that AVN has the potential to be an M cell-specific oral vaccine/drug delivery vehicle

Learning Meta Model for Zero- and Few-shot Face Anti-spoofing

Face anti-spoofing is crucial to the security of face recognition systems. Most previous methods formulate face anti-spoofing as a supervised learning problem to detect various predefined presentation attacks, which need large scale training data to cover as many attacks as possible. However, the trained model is easy to overfit several common attacks and is still vulnerable to unseen attacks. To overcome this challenge, the detector should: 1) learn discriminative features that can generalize to unseen spoofing types from predefined presentation attacks; 2) quickly adapt to new spoofing types by learning from both the predefined attacks and a few examples of the new spoofing types. Therefore, we define face anti-spoofing as a zero- and few-shot learning problem. In this paper, we propose a novel Adaptive Inner-update Meta Face Anti-Spoofing (AIM-FAS) method to tackle this problem through meta-learning. Specifically, AIM-FAS trains a meta-learner focusing on the task of detecting unseen spoofing types by learning from predefined living and spoofing faces and a few examples of new attacks. To assess the proposed approach, we propose several benchmarks for zero- and few-shot FAS. Experiments show its superior performances on the presented benchmarks to existing methods in existing zero-shot FAS protocols.Comment: Accepted by AAAI202

Postmortem biochemical and textural changes in the Patinopecten yessoensis adductor muscle (PYAM) during iced storage

Postmortem characteristics of Patinopecten yessoensis adductor muscle (PYAM) were evaluated by biochemical, chemical and textural changes during iced storage for 14 days. Triphosphate (ATP) and its breakdown products, K-value, total volatile basic nitrogen (TVB-N), pH, water-holding capacity (WHC), color, texture, protein degradation and cathepsin activities were monitored. K-value increased linearly from 5.9 ± 0.9% at day 0 to 28.1 ± 2.4% at day 2 and 70.2 ± 1.8% at day 12. Spoilage indicator TVB-N (mg/100 g) increased from 10.0 ± 0.6 to 34.6 ± 3.1 at day 12. Textural parameters (e.g., hardness, chewiness, springiness, adhesiveness, and shear force) followed a declining trend over the storage. The WHC decreased from 85.1 ± 3.1% at day 0 to 70.5 ± 1.8% at day 12. SDS-PAGE result indicated that proteolysis occurred in actin and myosin heavy chain (MHC) at day 14. Both cathepsin B and L increased throughout the iced storage, peaking at 1.47-fold and 1.08-fold, respectively, suggesting that cathepsin B and L played important roles in the deterioration of PYAM quality. The overall results indicated that PYAM was suitable to be consumed raw within the first 2 days, and to be processed in no more than 11 days

Deep Learning for Face Anti-Spoofing: A Survey

Face anti-spoofing (FAS) has lately attracted increasing attention due to its vital role in securing face recognition systems from presentation attacks (PAs). As more and more realistic PAs with novel types spring up, traditional FAS methods based on handcrafted features become unreliable due to their limited representation capacity. With the emergence of large-scale academic datasets in the recent decade, deep learning based FAS achieves remarkable performance and dominates this area. However, existing reviews in this field mainly focus on the handcrafted features, which are outdated and uninspiring for the progress of FAS community. In this paper, to stimulate future research, we present the first comprehensive review of recent advances in deep learning based FAS. It covers several novel and insightful components: 1) besides supervision with binary label (e.g., '0' for bonafide vs. '1' for PAs), we also investigate recent methods with pixel-wise supervision (e.g., pseudo depth map); 2) in addition to traditional intra-dataset evaluation, we collect and analyze the latest methods specially designed for domain generalization and open-set FAS; and 3) besides commercial RGB camera, we summarize the deep learning applications under multi-modal (e.g., depth and infrared) or specialized (e.g., light field and flash) sensors. We conclude this survey by emphasizing current open issues and highlighting potential prospects.Comment: IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI