Universal Detection Research Articles

Wheat Germ Agglutinin-Modified "Three-in-One" Multifunctional Probe Driven Broad-Spectrum and Flexible Immunochromatographic Diagnosis of viruses With High Sensitivity.

The conventional lateral flow assay (LFA) fails to the demands for the accurate screening of viruses as a result of its low sensitivity of colorimetric signal output and poor universality limited by antibody pairs. Here, a magnetically assisted dual-signal output LFA platform is developed for the ultrasensitive, universal, and flexible detection of viruses. A "three-in-one" multifunctional probe (MAuDQD) is prepared using a 180nm Fe3O4 core to load numerous Au nanoparticles (NPs) and two layers of QDs, which can substantially improve the sensitivity of LFA through coupling with the effects of magnetic enrichment and colorimetric/fluorescent enhancement. Wheat germ agglutinin (WGA)-modified MAuDQD attained the broad-spectrum capture viral membrane proteins and the colorimetric/fluorescent dual-mode detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and monkeypox virus (MPXV) on the LFA strip. In the colorimetric mode, the target viruses detected directly, with the visual sensitivity reaching 0.1-0.5ngmL-1 and the fluorescent mode supported quantitative analysis of SARS-CoV-2/MPXV with limits of detection decreasing to pgmL-1 level. Practicability of the MAuDQD@WGA-LFA is verified through the detection of 33 real clinical samples, showing the proposed assay has a great potential to become a sensitive, accurate, and universal tool for on-site monitoring of viral infections.

Reverse-transcription recombinase-aided amplification and CRISPR/Cas12a-based universal detection for fast screening and accurate identification of six pospiviroids infecting Solanaceae crops.

Pospiviroids, members of the genus Pospiviroid, can cause severe diseases in tomato and other Solanaceae crops, causing considerable economic losses worldwide. Six pospiviroids including potato spindle tuber viroid (PSTVd), tomato chlorotic dwarf viroid (TCDVd), tomato planta macho viroid (TPMVd), Columnea latent viroid (CLVd), pepper chat fruit viroid (PCFVd), and tomato apical stunt viroid (TASVd) are regulated in many countries and organizations. Rapid, accurate detection is thus crucial for controlling the spread of these pospiviroids. For simultaneous detection of these six pospiviroids, we developed a rapid, visual method that uses a reverse transcription recombinase-aided amplification (RT-RAA) assay coupled with a clustered regularly interspaced short palindromic repeats and CRISPR-associated protein 12a (CRISPR/Cas12a) system. In particular, this technique could achieve both universal detection and specific identification of the six target pospiviroids within 40 min. The universal detection could diagnose the six target pospiviroids in a single reaction, and the specific identification could identify each target pospiviroid without cross-reactivity of other pospiviroids. The sensitivity limits for the target pospiviroids detection with the proposed detection method were higher than those of the conventional reverse transcription-polymerase chain reaction (RT-PCR) method. We designed an RT-RAA-CRISPR/Cas12a-based universal detection method for both large-scale screening and accurate identification of the six target pospiviroids, which is appropriate for on-site detection. Our study results can aid in performing rapid, large-scale screening of multiple pests simultaneously. © 2024 Society of Chemical Industry.

A Versatile Method to Create Antibody/Split‐Enzyme Complexes and Its Application to a Rapid, Homogeneous, and Universal Electrochemical Immunosensing System

AbstractIn this study, a versatile method is developed to create an antibody/split‐enzyme complex for use in electrochemical immunosensors. SpyCatchers are genetically fused at each terminus of flavin adenine dinucleotide‐dependent glucose dehydrogenase (FAD‐GDH) and a protease recognition sequence is inserted into a loop region to prepare a soluble protein and the split GDH. SpyTag‐fused single‐chain variable fragments (scFvs) are employed, which leads to the simple preparation of antibody‐split enzyme complexes (split AECs). The addition of pentameric C‐reactive protein (CRP), as a representative multimeric protein, restored the GDH activity of the split AECs, as the two split AEC fragments formed active GDH when in close proximity. CRP in human serum is electrochemically detected within 5 min without any washing steps with a clinically relevant CRP detection range (0.03–1 mg dL−1). The detection system is expanded to detect hemoglobin, SARS‐CoV‐2 spike protein, and inactivated SARS‐CoV‐2 by exchanging the scFvs. These results show that the convenient preparation method of the split GDH and the split AEC by the insertion of the protease recognition sequence and the use of SpyCatcher/SpyTag can realize a rapid, homogeneous, and universal electrochemical detection system that can be integrated into a commercially available electrochemical glucose sensor.

An intelligent fluorescence sensing platform based on entropy-driven toehold-mediated strand displacement cycle reaction for point-of-care testing of miRNA

BackgroundMicroRNA (miRNA) has gradually become an emerging biomarker for early diagnosis and prognosis of various diseases due to its specific gene expression and high stability. With the development of molecular diagnosis and point-of-care testing (POCT) technology, developing simple, fast, sensitive, efficient, and low-cost miRNA sensors is of great significance for clinical applications and emergency rapid diagnosis. At present, entropy-driven toehold mediated chain displacement reaction, as a promising enzyme free isothermal amplification technique, is an important tool for ultra-sensitive biosensing applications. ResultsIn this study, we used gold nanoparticles (AuNPs) as carriers and quenchers, modified them using self-assembled triple chain composite substrates AuNPs@A@B1/B2, and used dual reporter molecules for cascade cyclic amplification to amplify fluorescence signals, which proposed a fluorescent biosensor based on this reaction and build an intelligent fluorescence sensing platform for rapid detection of miRNA. We designed a highly specific self-programmable sensor using the acute ischemic stroke (AIS) biomarker miRNA-125a-5p as a sample, and achieved sensitive detection of miRNA in the range of 0.01 μM∼10 μ M under optimal conditions. It broke through the traditional detection limitations of weak signals and liberated the fluorescence detection environment. SignificanceIn summary, this creative miRNA biosensor combined with POCT has demonstrated extraordinary detection potential, broad application prospects in the early diagnosis and prognosis monitoring of AIS, provides a novel miRNA universal detection strategy for the fields of biological and life sciences.

Interpretable semi-supervised clustering enables universal detection and intensity assessment of diverse aviation hazardous winds

The identification of aviation hazardous winds is crucial and challenging in air traffic management for assuring flight safety, particularly during the take-off and landing phases. Existing criteria are typically tailored for special wind types, and whether there exists a universal feature that can effectively detect diverse types of hazardous winds from radar/lidar observations remains as an open question. Here we propose an interpretable semi-supervised clustering paradigm to solve this problem, where the prior knowledge and probabilistic models of winds are integrated to overcome the bottleneck of scarce labels (pilot reports). Based on this paradigm, a set of high-dimensional hazard features is constructed to effectively identify the occurrence of diverse hazardous winds and assess the intensity metrics. Verification of the paradigm across various scenarios has highlighted its high adaptability to diverse input data and good generalizability to diverse geographical and climate zones.

Guide DNA dephosphorylation-modulated Pyrococcus furiosus Argonaute fluorescence biosensor for the detection of alkaline phosphatase and aflatoxins B1

Foodborne hazardous factors pose a significant risk to public health, emphasizing the need for the development of sensitive and user-friendly detection strategies to effectively manage and control these risks in the food supply chain. Pyrococcus furiosus argonaute (PfAgo)-based biosensing approaches have been extensively explored due to its built-in signal amplification. However, the property that PfAgo is a DNA-guided DNA endonuclease has enabled almost all the existing PfAgo-based reports to be used for the detection of nucleic acids. To lend PfAgo toolbox to extended non-nucleic acid detection, we systematically investigated the mechanism characteristic of PfAgo′ preference for guide DNA (gDNA) and proposed a gDNA dephosphorylation-modulated PfAgo sensor for the detection of non-nucleic acid targets. Our results indicated that PfAgo exhibits preference for 5′-phosphorylated gDNA at a specific ratio of PfAgo to gDNA concentration. Leveraging this PfAgo′ preference and the dephosphorylation activity of alkaline phosphatase (ALP), ALP could be detected as low as 2.7 U/L. Furthermore, the PfAgo was coupled with immunolabelled ALP to develop a PfAgo-based fluorescence immunosensor, which achieves aflatoxins B1 detection with a detection limit of 29.89 pg/mL and exhibits satisfactory recoveries in wheat and maize samples. The developed method broadens the application scope of PfAgo toolbox, and provides a simple, sensitive, and universal detection platform for a variety targets.

Ultrasensitive detection platform for Staphylococcus aureus based on DNAzyme tandem blocking CRISPR/Cas12a system

Detection methods based on CRISPR/Cas12a have been widely developed in the application of pathogenic microorganisms to guarantee food safety and public health. For sensitive detection, the CRISPR-based strategies are often in tandem with amplification methods. However, that may increase the detection time and the process may introduce nucleic acid contamination resulting in non-specific amplification. Herein, we established a sensitive S. aureus detection strategy based on the CRISPR/Cas12a system combined with DNAzyme. The activity of Cas12a is blocked by extending the spacer of crRNA (bcrRNA) and can be reactivated by Mn2+. NH2-modified S. aureus-specific aptamer was loaded on the surface of Fe3O4 MNPs (apt-Fe3O4 MNPs) and MnO2 NPs (apt-MnO2 NPs) by EDC/NHS chemistry. The S. aureus was captured to form apt-Fe3O4 MNPs/S. aureus/apt-MnO2 NPs complex and then MnO2 NPs were etched to release Mn2+ to activate DNAzyme. The active DNAzyme can cleave the hairpin structure in bcrRNA to recover the activity of the CRISPR/Cas system. By initiating the whole detection process by generating Mn2+ through nanoparticle etching, we established a rapid detection assay without nucleic acid extraction and amplification process. The proposed strategy has been applied in the ultrasensitive quantitative detection of S. aureus and has shown good performance with an LOD of 5 CFU/mL in 29 min. Besides, the proposed method can potentially be applied to other targets by simply changing the recognition element and has the prospect of developing a universal detection strategy.

Photothermal Backscatter Interferometry for Enhanced Detection in Capillary Electrophoresis.

Refractive index (RI) detection using backscatter interferometry (BSI) enables universal detection in capillary electrophoresis (CE). BSI detection is a versatile on-capillary approach that is easily integrated with capillary or microfluidic channels, straightforward to miniaturize, and inexpensive. The focused BSI light source can also double as the excitation source for fluorescence, enabling simultaneous universal (BSI) and specific (fluorescence) signals from the same detection volume. To improve BSI detection and expand orthogonal content, we integrate photothermal absorption with BSI detection. Nonradiative relaxation of an excited analyte releases heat into the surroundings, which modifies both the local RI and conductivity (viscosity) of the analyte zone. We recently showed that the BSI signal is sensitive to both RI and conductivity, which makes photothermal absorption a promising route to signal enhancement. Here, we use coaxially delivered BSI and photothermal absorption beams to characterize BSI, photothermal BSI, and fluorescence detection using the separation of test samples. We show that photothermal absorption leads to 3 orders of magnitude improvement in BSI detection limits at the powers studied and provides new opportunities for studying binding interactions with CE.

A Low voltage AC fault arc detection method based on multi-feature fusion

Abstract The complex electrical environment makes it difficult for most arc detectors on the market to accurately detect faulty arcs. A universal fault arc detection method based on machine learning (ML) algorithms is proposed in this paper. To address the potential hazards of fault arcs in the power system, firstly, the causes, time-domain, and frequency-domain characteristics of fault arcs were analyzed. Then, an arc experiment platform was built according to national standards, and current data was collected under various load conditions. Finally, the arc detection model was trained based on ML. The main contribution of this study is to explore a feature combination that is not affected by load type interference and has strong generalization ability for identifying fault arcs, clarify the key factors and feature extraction methods for identifying fault arcs, and develop a set of high-precision arc detection methods, providing strong guarantees for the stable operation of the power system.

A quantitative analysis of the improvement provided by comprehensive annotation on CT lesion detection using deep learning.

Data collected from hospitals are usually partially annotated by radiologists due to time constraints. Developing and evaluating deep learning models on these data may result in over or underestimation PURPOSE: We aimed to quantitatively investigate how the percentage of annotated lesions in CT images will influence the performance of universal lesion detection (ULD)algorithms. We trained a multi-view feature pyramid network with position-aware attention (MVP-Net) to perform ULD. Three versions of the DeepLesion dataset were created for training MVP-Net. Original DeepLesion Dataset (OriginalDL) is the publicly available, widely studied DeepLesion dataset that includes 32735 lesions in 4427 patients which were partially labeled during routine clinical practice. Enriched DeepLesion Dataset (EnrichedDL) is an enhanced dataset that features fully labeled at one or more time points for 4145 patients with 34317 lesions. UnionDL is the union of the OriginalDL and EnrichedDL with 54510 labeled lesions in 4427 patients. Each dataset was used separately to train MVP-Net, resulting in the following models: OriginalCNN (replicating the original result), EnrichedCNN (testing the effect of increased annotation), and UnionCNN (featuring the greatest number of annotations). Although the reported mean sensitivity of OriginalCNN was 84.3% using the OriginalDL testing set, the performance fell sharply when tested on the EnrichedDL testing set, yielding mean sensitivities of 56.1%, 66.0%, and 67.8% for OriginalCNN, EnrichedCNN, and UnionCNN, respectively. We also found that increasing the percentage of annotated lesions in the training set increased sensitivity, but the margin of increase in performance gradually diminished according to the powerlaw. We expanded and improved the existing DeepLesion dataset by annotating additional 21775 lesions, and we demonstrated that using fully labeled CT images avoided overestimation of MVP-Net's performance while increasing the algorithm's sensitivity, which may have a huge impact to the future CT lesion detection research. The annotated lesions are at https://github.com/ComputationalImageAnalysisLab/DeepLesionData.

Evaluation of Anti-CAR Linker mAbs for CAR T Monitoring after BiTEs/bsAbs and CAR T-Cell Pretreatment.

For the monitoring of chimeric antigen receptor (CAR) T-cell therapies, antigen-based CAR detection methods are usually applied. However, for each target-antigen, a separate detection system is required. Furthermore, when monitored CAR T-cells in the blood of patients treated with bispecific antibodies or T-cell engagers (bsAbs/BiTEs) recognize the same antigen, these methods produce false-positive results in clinical diagnostics. Anti-CAR-linker monoclonal antibodies (mAbs) targeting the linker sequence between the variable domains of the antigen binding CAR fragment promise a universal and unbiased CAR detection. To test this, we analyzed clinical specimens of all BCMA- and CD19-targeting CAR T-cell products currently approved for clinical use. We found a highly specific and sensitive CAR detection using anti-CAR-linker mAb in blood cells from patients treated with Ide-cel, Tisa-cel, Axi-cel, Brexu-cel, and Liso-cel. For Ide-cel and Tisa-cel, the sensitivity was significantly lower compared to that for antigen-based CAR detection assays. Strikingly, the specificity of anti-CAR linker mAb was not affected by the simultaneous presence of bispecific blinatumomab or teclistamab for Axi-cel, Brexu-cel, Liso-cel, or Ide-cel, respectively. Cilta-cel (containing a monomeric G4S-CAR linker) could not be detected by anti-CAR linker mAb. In conclusion, anti-CAR-linker mAbs are highly specific and useful for CAR T-cell monitoring but are not universally applicable.

Hook-Like DNAzyme-Activated Autocatalytic Biosensor for the Universal Detection of Pathogenic Bacteria.

DNAzyme-based assays have found extensive utility in pathogenic bacteria detection but often suffer from limited sensitivity and specificity. The integration of a signal amplification strategy could address this challenge, while the existing combination methods require extensive modification to accommodate various DNAzymes, limiting the wide-spectrum bacteria detection. We introduced a novel hook-like DNAzyme-activated autocatalytic nucleic acid circuit for universal pathogenic bacteria detection. The hook-like connector DNA was employed to seamlessly integrate the recognition element DNAzyme with the isothermal enzyme-free autocatalytic hybridization chain reaction and catalytic hairpin assembly for robust exponential signal amplification. This innovative autocatalytic circuit substantially amplifies the output signals from the DNAzyme recognition module, effectively overcoming DNAzyme's inherent sensitivity constraints in pathogen identification. The biosensor exhibits a strong linear response within a range of 1.5 × 103 to 3.7 × 107 CFU/mL, achieving a detection limit of 1.3 × 103 CFU/mL. Noted that the sensor's adaptability as a universal detection platform is established by simply modifying the hook-like connector module, enabling the detection of various pathogenic bacteria of considerable public health importance reported by the World Health Organization, including Escherichia coli, Staphylococcus aureus, Klebsiella pneumoniae, and Salmonella typhimurium. Additionally, the specificity of DNAzyme in bacterial detection is markedly improved due to the signal amplification process of the autocatalytic circuit. This hook-like DNAzyme-activated autocatalytic platform presents a versatile, sensitive, and specific approach for pathogenic bacteria detection, promising to significantly expand the applications of DNAzyme in bacteria detection.

Comparative Analysis and Characteristic Verification of the Quality Standard of Milk Thistle Extract

Introduction:: Milk thistle extract is widely used in dietary supplements due to its strong hepatoprotective effect. There are some differences in the quality characteristics and standard values of milk thistle extract set by pharmacopoeias and standards in various countries and regions. Method:: To promote the standardization of quality standards for milk thistle extract, this paper summarized and compared the standard characteristics and related values settings in four different standards. At the same time, actual samples were tested for the characteristics that appeared more frequently in standard. We found that the ratio algorithm of silymarin set in the pharmacopoeia standards was not reasonable and could blur the quality of related products from the test results, especially without limiting the total amount of silymarin in the standard. Results:: We suggested setting standard values for all six silymarin characteristics, establishing more universal detection methods, and preparing relevant reference materials to match the detection methods. Based on the comparison of standards and test data, the characteristics of the ethanol content, ethyl acetate content, methanol insoluble substances, heavy metal content, and microbial characteristics should draw more attention and should be considered in the future for setting newly formulated standards. Conclusion:: This paper lays the data foundation for developing higher quality standards for milk thistle extract, which plays an important role in promoting the international trade of milk thistle extract.

Unraveling the influence of CRISPR/Cas13a reaction components on enhancing trans-cleavage activity for ultrasensitive on-chip RNA detection.

The CRISPR/Cas13 nucleases have been widely documented for nucleic acid detection. Understanding the intricacies of CRISPR/Cas13's reaction components is pivotal for harnessing its full potential for biosensing applications. Herein, we report onthe influence of CRISPR/Cas13a reaction components on its trans-cleavage activity and the development of an on-chip total internal reflection fluorescence microscopy (TIRFM)-powered RNA sensing system. We used SARS-CoV-2 synthetic RNA and pseudovirus as a model system. Our results show that optimizing Mg2+ concentration, reporter length, and crRNA combination significantly improves the detection sensitivity. Under optimized conditions, we detected 100 fM unamplified SARS-CoV-2 synthetic RNA using a microtiter plate reader. To furtherimprove sensitivity and provide a new amplification-free RNA sensing toolbox, we developed a TIRFM-based amplification-free RNA sensing system. We were able to detect RNA down to 100 aM. Furthermore, the TIRM-based detection system developed in this study is 1000-fold more sensitive than the off-coverslip assay. The possible clinical applicability of the system was demonstrated by detecting SARS-CoV-2 pseudovirus RNA. Our proposed sensing system has the potential to detect any target RNA with slight modifications to the existing setup, providing a universal RNA detection platform.

A novel mediator probe and universal fluorescence probe (MP-UP) system for highly sensitive and versatile nucleic acid detection

Real-time qPCR and digital PCR are powerful techniques for detecting nucleic acids with high specificity and sensitivity, relying on sequence-specific labeled probes. However, each target requires the design and synthesis of specific probes, incurring significant costs. Therefore, the development of a specific and universal detection technique is both urgent and beneficial. Previously, a universal detection method was developed using a modified mediator probe and a universal hairpin reporter. However, our experiments uncovered that the mediator primers, generated by Taq polymerase cleaving mediator probes, deviate from the expected sequence, indicating a severe compromise in detection ability. Additionally, the high cost of the modified mediator probe and hairpin reporter impedes the practicality of their method. To overcome these challenges, we have developed a novel universal detection method (MP-UP) using Taqman probes, unmodified mediator probes and helper target, enabling the utilization of all mediator primers to produce abundant fluorescence. Enhanced MP-UP qPCR with an increased signal-to-noise ratio, MP-UP ddPCR, and multiplex MP-UP were established, exhibiting remarkable sensitivity, specificity, and precision comparable to specific probe assays or commercial testing kit. The mediator probe and helper target cost only one-tenth of a specific Taqman probe, rendering it feasible to replace the latter in specific nucleic acid detection.

UniverDetect: Universal landmark detection method for multidomain X-ray images

Landmark detection using X-ray imaging is vital for disease screening, treatment, and prognosis. It provides a framework for subsequent tasks, including segmentation, classification, and target detection. Existing landmark detection methods exhibit limits with the increasing number of X-ray examiners. Not only they serve specific data types, but also their ability to learn the complete semantic information from this dataset is limited, potentially affecting their widespread adoption. We address these challenges by proposing a universal landmark detection model for multidomain X-ray imaging, UniverDetect. By progressively acquiring local semantic information, global semantic insights, and anatomical structure knowledge at various levels, the model ensured that the detected landmarks were closely aligned with the ground-truth labels. UniverDetect consists of three key components. The landmark detection module (LDM) utilizes a U-net network equipped with our innovative pyramid depthwise separable (PDS) convolution module for initial landmark detection. The landmark refinement module (LRM) integrates a fine-tuning module comprising continuously extended convolution blocks. Finally, the landmark correction module (LCM) incorporates a graph convolutional network (GCN) to rectify offset errors during partial landmark detection. An inherent feature of this model is its domain- generalization capability, which enables continuous learning across diverse domains. This model can concurrently learn from eight domains, covering 118 landmarks within a diverse dataset of 5969 images. Extensive experiments on multiple datasets demonstrate that this method consistently outperforms state-of-the-art approaches. This study presents a versatile and effective solution to reduce doctors’ workload while providing precise quantitative analysis.

From single to universal: tiny lesion detection in medical imaging

Accurate and automatic detection of tiny lesions in medical imaging plays a critical role in comprehensive cancer diagnosis, staging, treatment, follow-up, and prognosis. Numerous deep learning (DL) based studies have focused on single lesions, providing highly sensitive identification and precise localization. On the other hand, some recent studies have started to concentrate on whole-body lesions, as they could provide systemic clinical support. This paper presents a single-to-universal review of DL studies on tiny lesion detection in medical imaging, with a particular emphasis on detection models and techniques, as well as the data-related aspects such as modality, dimension, and dataset. A wide range of tasks are covered, including traditional single lesion detection tasks such as lung nodules, breast masses, thyroid nodules, and diseased lymph nodes, as well as the emerging task of universal lesion detection. Moreover, the paper provides in-depth analysis and discussion, with the hope of inspiring future research endeavors.

Ultra-fast detection of pathogens and protein biomarkers using a low-cost silicon plasmonic biosensing platform

In this paper, we report, for the first time to our knowledge, a new versatile and future proof diagnostic platform for diverse applications and heterogeneous biological targets. This platform relies on a plasmonic-augmented silicon photonic biochip to detect bacteria and protein biomarkers within minutes. To demonstrate the potential of this platform in diverse applications, we demonstrate the detection of two heterogeneous targets, the bacterium Escherichia coli (E. coli) and the molecule C-reactive protein (CRP) using a universal detection method. E. coli is one of the most commonly encountered bacterial pathogens involved in food monitoring, food born infections and water contamination applications, while CRP is a well-established disease severity indicator frequently used in common clinical practice. The biochip used is fully compatible with CMOS semiconductor manufacturing, while it hosts biosensors arrays for any combination of detection assays. Each biosensor exploits a 70 μm long aluminum plasmonic transducer within a silicon nitride waveguide-based Mach–Zehnder Interferometer. Each aluminum surface was silanized and biomodified with specific antibodies. Biosensing experiments revealed that CRP can be detected in diverse sample mediums, and E. coli detection was achieved in buffer at concentrations as low as 10 cells/ml within 13–25 min. The results are in good agreement with preceding numerical simulations. The modular nature of the reported biosensing platform makes it scalable and customizable, allowing nearly any combination of diagnostic tests targeting pathogens and proteins to be integrated on the same biochip for food quality monitoring, environmental monitoring, drug discovery and modern cell therapy manufacturing, or biomedical applications.

A universal fluorescence biosensor based on rolling circle amplification and locking probe for DNA detection.

Astable DNA signal amplification sensor was developed on account of rolling circle amplification (RCA). This sensor includes target DNA-controlled rolling circle amplification technology and locking probe DNA replacement technology, which can be used to detect DNA fragments with genetic information, thus constructing a biosensor for universal detection of DNA. This study takes the homologous DNA of human immunodeficiency virus (HIV) and let-7a as examples to describe this biosensor. The padlock probe is first cyclized by T4 DNA ligase in response to the target's reaction with it. Then, rolling cycle amplification is initiated by Phi29 DNA polymerase, resulting in the formation of a lengthy chain with several triggers. These triggers can open the locked probe LP1 with the fluorescence signal turned off, so that it can continue to react with H2 to form a stable H1-H2 double strand. This regulates the distance between B-DNA modified by the quenching group and H1 modified by fluorescent group, and the fluorescence signal is recovered.

Confused and disentangled distribution alignment for unsupervised universal adaptive object detection

Universal domain adaptive object detection (UniDAOD) is a more challenging and realistic problem than traditional domain adaptive object detection (DAOD), aiming to transfer the knowledge from the well-labeled source domain to the unlabeled target domain without any prior knowledge of label sets. Intuitively, the main challenge of UniDAOD is to eliminate the domain shift and suppress the interference caused by the category shift induced by private classes (i.e., classes only existed in one domain). In the current study, we propose a simple but effective CODE framework, namely Confused and Disentangled Extraction, for alleviating this issue. Specifically, we propose the virtual adversarial adaptation module, characterized by incorporating virtual domain labels within the domain classifier for unaligned samples. This confuses the domain classifier, effectively addressing the issue of converging to local optima resulting from equilibrium challenges and consequently narrowing the domain shift. Simultaneously, we introduce the entropy margin separation module, which utilizes the distinctiveness of category predictions as a disentangled factor. This enables the automatic discovery of private classes in each domain, suppressing interference during the adaptation process. Experiments on four universal scenarios (i.e., closed-set, partial-set, open-partial-set, and open-set) show that CODE obtains a significant performance gain over original DAOD detectors.