Detection of prostate specific antigen in serum at the femto-gram per milliliter level using the intrinsic amplification of a field-effect enzymatic immuno-sensing system

Ultrasensitive detection of prostate specific antigen (PSA) has been achieved by applying the field-effectenzymatic detection (FEED) technique to the sandwich immuno-sensing technique. The voltage-controlled intrinsic amplification provided by FEED enabled the detection of PSA contained in serumon the femto-gram/mL level. Two electrochemical approaches used to obtain the amperometric detec-tion signal resulted in similar detection limits and sensitivities. The lowest PSA detection limit achievedwas 27 fg mL-1. The high selectivity of the detection system was reflected in the fact that PSA detectionwas successful on the fg mL-1level, where biological substances other than PSA had a 1-million-foldhigher concentration. Electron transfer through the immunological sandwich nanostructure has beenobserved in the detection of biomarkers. However, our results showed that electron transfer through thenanostructure could be controlled using an external voltage, leading to an ultralow detection limit for PSA

Field-Effect Amperometric Immuno-Detection of Protein Biomarker

The field-effect enzymatic detection technique has been applied to the amperometric immunoassay of the cancer biomarker, carcinoma antigen 125 (CA 125). The detection adopted a reagentless approach, in which the analyte, CA 125, was immobilized on the detecting electrode, which was modified using carbon nanotubes, and the detection signal was obtained by measuring the reduction peak current of the enzyme that was used to label the antibody. A gating voltage was applied to the detecting electrode, inducing increase in the signal current and therefore providing amplification of the detection signal. The voltage-controlled signal amplification of the detection system has increased the sensitivity and lowered the detection limit of the system. A detection limit of 0.9U/ml was obtained in the work

Detection of prostate specific antigen in serum at the femto-gram per milliliter level using the intrinsic amplification of a field-effect enzymatic immuno-sensing system

Ultrasensitive detection of prostate specific antigen (PSA) has been achieved by applying the field-effectenzymatic detection (FEED) technique to the sandwich immuno-sensing technique. The voltage-controlled intrinsic amplification provided by FEED enabled the detection of PSA contained in serumon the femto-gram/mL level. Two electrochemical approaches used to obtain the amperometric detec-tion signal resulted in similar detection limits and sensitivities. The lowest PSA detection limit achievedwas 27 fg mL-1. The high selectivity of the detection system was reflected in the fact that PSA detectionwas successful on the fg mL-1level, where biological substances other than PSA had a 1-million-foldhigher concentration. Electron transfer through the immunological sandwich nanostructure has beenobserved in the detection of biomarkers. However, our results showed that electron transfer through thenanostructure could be controlled using an external voltage, leading to an ultralow detection limit for PSA

ConaCLIP: Exploring Distillation of Fully-Connected Knowledge Interaction Graph for Lightweight Text-Image Retrieval

Large-scale pre-trained text-image models with dual-encoder architectures (such as CLIP) are typically adopted for various vision-language applications, including text-image retrieval. However,these models are still less practical on edge devices or for real-time situations, due to the substantial indexing and inference time and the large consumption of computational resources. Although knowledge distillation techniques have been widely utilized for uni-modal model compression, how to expand them to the situation when the numbers of modalities and teachers/students are doubled has been rarely studied. In this paper, we conduct comprehensive experiments on this topic and propose the fully-Connected knowledge interaction graph (Cona) technique for cross-modal pre-training distillation. Based on our findings, the resulting ConaCLIP achieves SOTA performances on the widely-used Flickr30K and MSCOCO benchmarks under the lightweight setting. An industry application of our method on an e-commercial platform further demonstrates the significant effectiveness of ConaCLIP.Comment: ACL 2023 Industry Trac

Mediator-less immunodetection with voltage-controlled intrinsic amplification for ultrasensitive and rapid detection of microorganism pathogens

A mediator-less immunodetection method for microorganisms is realized by incorporating the newly developed field-effect enzymatic detection (FEED) technique with the conventional electrochemical immunosensing approach. The gating voltage of FEED facilitates the transduction of electrical signal through the bulky immune complex so that the detection does not rely on the use of mediators or other diffusional substances. The voltage-controlled intrinsic amplification provided by the detection system allows detection in low-concentration samples without target pre-enrichment, leading to ultrasensitive and rapid detection. The detection approach is demonstrated with E. coliO157:H7, a model microorganism, in milk with an estimated detection limit of 20 CFU mL−1 (where CFU is a colony-forming unit) without performing sample pre-enrichment and centrifugation of sample followed by the resuspension of the pellet in a buffer solution, resulting in a significantly shortened assay time of 67 min. Optimizing the gating voltage resulted in the detection of 12 CFU mL−1 of the bacterium in milk. The novel detection approach can be used as a detection platform for ultrasensitive, specific and rapid detection of microorganism pathogens

An Efficient FPGA-based Accelerator for Deep Forest

Deep Forest is a prominent machine learning algorithm known for its high accuracy in forecasting. Compared with deep neural networks, Deep Forest has almost no multiplication operations and has better performance on small datasets. However, due to the deep structure and large forest quantity, it suffers from large amounts of calculation and memory consumption. In this paper, an efficient hardware accelerator is proposed for deep forest models, which is also the first work to implement Deep Forest on FPGA. Firstly, a delicate node computing unit (NCU) is designed to improve inference speed. Secondly, based on NCU, an efficient architecture and an adaptive dataflow are proposed, in order to alleviate the problem of node computing imbalance in the classification process. Moreover, an optimized storage scheme in this design also improves hardware utilization and power efficiency. The proposed design is implemented on an FPGA board, Intel Stratix V, and it is evaluated by two typical datasets, ADULT and Face Mask Detection. The experimental results show that the proposed design can achieve around 40x speedup compared to that on a 40 cores high performance x86 CPU.Comment: 5 pages, 5 figures, conferenc

RESEARCH ON QUANTIFICATION OF HAZOP DEVIATION BASED ON A DYNAMIC SIMULATION AND NEURAL NETWORK

Hazard and operability (HAZOP) analysis has become more significant as the complexity of process technology has increased. However, traditional HAZOP analysis has limitations in quantifying the deviations. This work introduces artificial neural networks (ANNs) and Aspen HYSYS to explore the feasibility of HAZOP deviation quantification. With the proposed HAZOP automatic hazard analyzer (HAZOP-AHA) method, the conventional HAZOP analysis of the target process is first carried out. Second, the HYSYS dynamic model of the relevant process is established to reflect the influence of process parameters on target parameters. Third, to solve the problem of deviation identification based on multi-attribute and a large dataset, we use the ANN to process the input data. Finally, HAZOP deviation can be quantified and predicted. The method is verified by the industrial alkylation of benzene with propene to cumene. The results show that the predicted deviation severity can be close to the actual deviation severity, and the accuracy of prediction can reach nearly 100%. Thus, the method can diminish the probability of conflagration, burst, and liquid leakage