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Immunoassay has the advantages of high sensitivity, high specificity, and simple operation, and has been widely used in the detection of mycotoxins. For several years, time-resolved fluorescence immunochromatography (TRFIA) paper-based sensors have attracted much attention as a simple and low-cost field detection technology. However, a traditional TRFIA paper-based sensor is based on antibody labeling, which cannot easily meet the current detection requirements. A second antibody labeling method was used to amplify the fluorescence signal and improve the detection sensitivity. Polystyrene fluorescent microspheres were combined with sheep anti-mouse IgG to prepare fluorescent probes (Eu-IgGs). After the probe fully reacted with the antibody (Eu-IgGs-Abs) in the sample cell, it was deployed on the paper-based sensor using chromatography. Eu-IgGs-Abs that were not bound to the target were captured on the T-line, while those that were bound were captured on the C-line. The paper-based sensor reflected the corresponding fluorescence intensity change. Because a single molecule of the deoxynivalenol antibody could bind to multiple Eu-IgGs, this method could amplify the fluorescence signal intensity on the unit antibody and improve the detection sensitivity. The working standard curve of the sensor was established under the optimum working conditions. It showed the lower limit of detection and higher recovery rate when it was applied to actual samples and compared with other methods. This sensor has the advantages of high sensitivity, good accuracy, and good specificity, saving the amount of antibody consumed and being suitable for rapid field detection of deoxynivalenol.

This chapter contains sections titled: Introduction Monitoring Methods Mechanism of Protein Binding and Degradation in Soil Systems Monitoring Protein in Soil with Immunoassays A Case Study Application of Immunoassay in Soil Accumulation Studies References

Digital breast tomosynthesis (DBT) has been widely implemented in place of 2D mammography, although it is less effective in women with extremely dense breasts. Breast ultrasound detects additional early-stage, invasive breast cancers when combined with mammography; however, its relevant limitations, including the shortage of trained operators, operator dependence and small field of view, have limited its widespread implementation. Automated breast sonography (ABS) is a promising technique but the time to interpret and false-positive rates need to be improved. Supplemental screening with contrast-enhanced magnetic resonance imaging (MRI) in high-risk women reduces late-stage disease; abbreviated MRI protocols may reduce cost and increase accessibility to women of average risk with dense breasts. Contrast-enhanced digital mammography (CEDM) and molecular breast imaging improve cancer detection but require further validation for screening and direct biopsy guidance should be implemented for any screening modality. This article reviews the status of screening women with dense breasts.Key Points• The sensitivity of mammography is reduced in women with dense breasts. Supplemental screening with US detects early-stage, invasive breast cancers.• Tomosynthesis reduces recall rate and increases cancer detection rate but is less effective in women with extremely dense breasts.• Screening MRI improves early diagnosis of breast cancer more than ultrasound and is currently recommended for women at high risk. Risk assessment is needed, to include breast density, to ascertain who should start early annual MRI screening.

An assembled-aptasensor based on Fe 3 O 4 @Au@Ag nanocomposites grafting onto the gold foil was prepared, which can be developed into a universal approach for sensitive and rapid detection of various pathogenic bacteria, such as Escherichia coli ( E. coli ), Salmonella typhimurium ( S. typhimurium ), Staphylococcus aureus ( S. aureus ), Listeria monocytogenes ( L. monocytogenes ), Pseudomonas aeruginosa ( P. aeruginosa ), and Shigella flexneri ( S. flexneri ). Firstly, the gold foil paper was modified with thiolated capture probe and SERS tag in proportion, and at the same time, the specific thiolated aptamer probe for corresponding pathogenic bacteria was fixed with Fe 3 O 4 @Au@Ag nanocomposites. An obvious Raman signal can be subsequently increased about 10 6 times by the external electromagnetic field enhancement at the “hot spots” caused by the hybridization of aptamer and capture probe. But in the presence of target pathogenic bacteria, Raman intensity will decrease as Fe 3 O 4 @Au@Ag nanocomposites are dissociated from gold foil. Thus, all of the concentrations of the six kinds of pathogenic bacteria both in PBS and liquorice extract showed an obvious negative linear correlation with the Raman intensity of SERS tag in the range of 10–10 7  CFU/mL with detection limits were all lower than 10 CFU/mL. And there was no significant difference between our method and the plate counting method. Besides, the assembled-aptasensor had superior specific recognition ability even in the mixed interfering bacteria. Our study showed that this assembled-aptasensor had good specific detection ability to a variety of foodborne pathogens based on magnetic field-assisted SERS technique, which can be used for rapid and sensitive detection of a variety of pathogens in complex substrates. Graphical abstract

Early diagnosis of the infection caused by human immunodeficiency virus type-1 (HIV-1) is vital to achieve efficient therapeutic treatment and limit the disease spreading when the viremia is at its highest level. To this end, a point-of-care HIV-1 detection carried out with label-free, low-cost, and ultra-sensitive screening technologies would be of great relevance. Herein, a label-free single molecule detection of HIV-1 p24 capsid protein with a large (wide-field) single-molecule transistor (SiMoT) sensor is proposed. The system is based on an electrolyte-gated field-effect transistor whose gate is bio-functionalized with the antibody against the HIV-1 p24 capsid protein. The device exhibits a limit of detection of a single protein and a limit of quantification in the 10 molecule range. This study paves the way for a low-cost technology that can quantify, with single-molecule precision, the transition of a biological organism from being “healthy” to being “diseased” by tracking a target biomarker. This can open to the possibility of performing the earliest possible diagnosis.

A bstract We present a general procedure for constructing tensor networks that accurately reproduce holographic states in conformal field theories (CFTs). Given a state in a large- N CFT with a static, semiclassical gravitational dual, we build a tensor network by an iterative series of approximations that eliminate redundant degrees of freedom and minimize the bond dimensions of the resulting network. We argue that the bond dimensions of the tensor network will match the areas of the corresponding bulk surfaces. For “tree” tensor networks (i.e., those that are constructed by discretizing spacetime with non­ intersecting Ryu-Takayanagi surfaces), our arguments can be made rigorous using a version of one-shot entanglement distillation in the CFT. Using the known quantum error correcting properties of AdS/CFT, we show that bulk legs can be added to the tensor networks to create holographic quantum error correcting codes. These codes behave similarly to previous holographic tensor network toy models, but describe actual bulk excitations in continuum AdS/CFT. By assuming some natural generalizations of the “holographic entanglement of purification” conjecture, we are able to construct tensor networks for more general bulk discretizations, leading to finer-grained networks that partition the information content of a Ryu-Takayanagi surface into tensor-factorized subregions. While the granularity of such a tensor network must be set larger than the string/Planck scales, we expect that it can be chosen to lie well below the AdS scale. However, we also prove a no-go theorem which shows that the bulk-to-boundary maps cannot all be isometries in a tensor network with intersecting Ryu-Takayanagi surfaces.

Detection of source horizontal boundaries is a common feature in the interpretation of magnetic and gravity data. A wide range of derivative- and phase-based methods are available to solve this problem. Here, we compare the effectiveness of the commonly used methods, and introduce a method based on the logistic function and the horizontal gradient amplitude, which shows improved performance as a boundary detection filter. The effectiveness of the proposed filter is demonstrated by evaluating synthetic examples and a real example from the Central Puget Lowland (United States). The main advantage of this method is that it provides high-resolution results, and can avoid producing spurious boundaries in the output maps.

Background Above-ground biomass (AGB) is a basic agronomic parameter for field investigation and is frequently used to indicate crop growth status, the effects of agricultural management practices, and the ability to sequester carbon above and below ground. The conventional way to obtain AGB is to use destructive sampling methods that require manual harvesting of crops, weighing, and recording, which makes large-area, long-term measurements challenging and time consuming. However, with the diversity of platforms and sensors and the improvements in spatial and spectral resolution, remote sensing is now regarded as the best technical means for monitoring and estimating AGB over large areas. Results In this study, we used structural and spectral information provided by remote sensing from an unmanned aerial vehicle (UAV) in combination with machine learning to estimate maize biomass. Of the 14 predictor variables, six were selected to create a model by using a recursive feature elimination algorithm. Four machine-learning regression algorithms (multiple linear regression, support vector machine, artificial neural network, and random forest) were evaluated and compared to create a suitable model, following which we tested whether the two sampling methods influence the training model. To estimate the AGB of maize, we propose an improved method for extracting plant height from UAV images and a volumetric indicator (i.e., BIOVP). The results show that (1) the random forest model gave the most balanced results, with low error and a high ratio of the explained variance for both the training set and the test set. (2) BIOVP can retain the largest strength effect on the AGB estimate in four different machine learning models by using importance analysis of predictors. (3) Comparing the plant heights calculated by the three methods with manual ground-based measurements shows that the proposed method increased the ratio of the explained variance and reduced errors. Conclusions These results lead us to conclude that the combination of machine learning with UAV remote sensing is a promising alternative for estimating AGB. This work suggests that structural and spectral information can be considered simultaneously rather than separately when estimating biophysical crop parameters.

Background Field phenotyping by remote sensing has received increased interest in recent years with the possibility of achieving high-throughput analysis of crop fields. Along with the various technological developments, the application of machine learning methods for image analysis has enhanced the potential for quantitative assessment of a multitude of crop traits. For wheat breeding purposes, assessing the production of wheat spikes, as the grain-bearing organ, is a useful proxy measure of grain production. Thus, being able to detect and characterize spikes from images of wheat fields is an essential component in a wheat breeding pipeline for the selection of high yielding varieties. Results We have applied a deep learning approach to accurately detect, count and analyze wheat spikes for yield estimation. We have tested the approach on a set of images of wheat field trial comprising 10 varieties subjected to three fertilizer treatments. The images have been captured over one season, using high definition RGB cameras mounted on a land-based imaging platform, and viewing the wheat plots from an oblique angle. A subset of in-field images has been accurately labeled by manually annotating all the spike regions. This annotated dataset, called SPIKE, is then used to train four region-based Convolutional Neural Networks (R-CNN) which take, as input, images of wheat plots, and accurately detect and count spike regions in each plot. The CNNs also output the spike density and a classification probability for each plot. Using the same R-CNN architecture, four different models were generated based on four different datasets of training and testing images captured at various growth stages. Despite the challenging field imaging conditions, e.g., variable illumination conditions, high spike occlusion, and complex background, the four R-CNN models achieve an average detection accuracy ranging from 88 to 94 % across different sets of test images. The most robust R-CNN model, which achieved the highest accuracy, is then selected to study the variation in spike production over 10 wheat varieties and three treatments. The SPIKE dataset and the trained CNN are the main contributions of this paper. Conclusion With the availability of good training datasets such us the SPIKE dataset proposed in this article, deep learning techniques can achieve high accuracy in detecting and counting spikes from complex wheat field images. The proposed robust R-CNN model, which has been trained on spike images captured during different growth stages, is optimized for application to a wider variety of field scenarios. It accurately quantifies the differences in yield produced by the 10 varieties we have studied, and their respective responses to fertilizer treatment. We have also observed that the other R-CNN models exhibit more specialized performances. The data set and the R-CNN model, which we make publicly available, have the potential to greatly benefit plant breeders by facilitating the high throughput selection of high yielding varieties.

Since two‐dimensionalal (2D) materials have distinct chemical and physical properties, they are widely used in various sectors of modern technologies. In the domain of diagnostic biodevices, particularly for point‐of‐care (PoC) biomedical diagnostics, 2D‐based field‐effect transistor biosensors (bio‐FETs) demonstrate substantial potential. Here, in this review article, the operational mechanisms and detection capabilities of biosensing devices utilizing graphene, transition metal dichalcogenides (TMDCs), black phosphorus, and other 2D materials are addressed in detail. The incorporation of these materials into FET‐based biosensors offers significant advantages, including low detection limits (LOD), real‐time monitoring, label‐free diagnosis, and exceptional selectivity. The review also highlights the diverse applications of these biosensors, ranging from conventional to wearable devices, underscoring the versatility of 2D material‐based FET devices. Additionally, the review provides a comprehensive assessment of the limitations and challenges faced by these devices, along with insights into future prospects and advancements. Notably, a detailed comparison of FET‐based biosensors is tabulated along with various other biosensing platforms and their working mechanisms. Ultimately, this review aims to stimulate further research and innovation in this field while educating the scientific community about the latest advancements in 2D materials‐based biosensors. This article explores the working mechanism and detecting capacities of FET biosensors made from diverse 2D materials such as graphene, TMDCs, and black phosphorus. These materials enable the sensors to achieve low detection limits, real‐time monitoring, label‐free diagnostics, and great selectivity. It also discusses constraints and future directions, paving the path for advances in biosensor technology.