Using multiple methods to assess detection probabilities of forest-floor wildlife

Many previous comparisons of multiple sampling methods have assumed that detection probabilities for each method are either constant or equal to one. We used 4 sampling methods to estimate detection probabilities for forest-floor dwelling amphibians, reptiles, and small mammals. We investigated associations between seasonality and precipitation on species detection and explored sample design tradeoffs for future studies. Although we captured 25 species, we could reliably detect (detection probability >0.15) only northern short-tailed shrews (Blarina brevicauda) and pygmy and masked shrews (Sorex spp.) using drift fences and red-backed salamanders (Plethodon cinereus) using visual encounter surveys (VES). The use of multiple sampling methods improved detection probabilities for only red-backed salamanders ( = 0.32, 95% CI: 0.24–0.38, = 0.38, 95% CI: 0.32−0.44). Parameter estimates indicated detection of both shrew species was positively related to increased precipitation. Detection probabilities for pygmy and masked shrews and red-backed salamanders were positively and negatively associated with date, respectively. Our power analysis revealed that sampling during rain events increased the power of detecting a change in sorid occupancy by ≥40% (α = 0.05). Our results demonstrate the need to incorporate species detectability when comparing the effectiveness of different trapping methodologies. Furthermore, our study highlights the utility of power analyses for exploring study design tradeoffs for research and monitoring programs. © 2011 The Wildlife Society.     

Blood plasma resonance Raman spectroscopy combined with multivariate analysis for esophageal cancer detection

We herein report a novel, reliable and inexpensive method for detecting esophageal cancer using blood plasma resonance Raman spectroscopy combined with multivariate analysis methods. The blood plasma samples were divided into late stage cancer group (n = 164), early stage cancer group (n = 35) and normal group (n = 135) based on clinical pathological diagnosis. Using a specially designed quartz capillary tube as sample holder, we obtained higher quality resonance Raman spectra of blood plasma than existing method. The study demonstrated that the carotenoids levels in blood plasma were reduced in esophageal cancer patients. The area under the receiver operating characteristic curve (and 95% confidence interval) calculated by wavenumber selection and principal component analysis combined with linear discriminant analysis (PC-LDA) algorithm were 0.894 (0.858-0.929), 0.901 (0.841-0.960) and 0.871 (0.799-0.942) for differentiating late cancer from normal, late cancer from early cancer, and early cancer from normal respectively. The contribution from the two carotenoids wavenumber regions of 1155 and 1515 cm⁻¹ were more than 84.2%. The results show that the plasma carotenoids could be a potential biomarker for screening esophageal cancer using resonance Raman spectroscopy combined with wavenumber selection and PC-LDA algorithms.      

Label-free detection of living cervical cells based on microfluidic device with terahertz spectroscopy

Early diagnosis of cervical cancer is essential for a good prognosis. Terahertz wave detection technology is a nondestructive and label-free physical detection technology, which can detect and monitor the cancer cells in real time, especially for patients with deep or inaccessible tumors. In this study, a single-cell-layer microfluidic device was developed. After replacing the optical clearing agent, the characteristics of H8, HeLa and SiHa cell lines in adherent and suspended states were detected. Additionally, the absorption increased with increasing cell density. For the mixed suspension cell samples, principal component analysis–support vector machine method was used to identify benign and malignant cell component. After living cells formaldehyde, changes in cell membrane permeability were evaluated to identify the cell survival status (i.e., dead or living) based on terahertz spectroscopy amplitude differences. Therefore, extending the terahertz spectrum detection to the molecular level can characterize the life essence of cells and tissues.     

In vivo detection of tumor boundary using ultrahigh‐resolution optical coherence angiography and fluorescence imaging

Accurate detection of early tumor margin is of great preclinical and clinical implications for predicting the survival rate of subjects and assessing the response of tumor microenvironment to chemotherapy or radiation therapy. Here, we report a multimodality optical imaging study on in vivo detection of tumor boundary by analyzing neoangiogenesis of tumor microenvironment (microangiography), microcirculatory blood flow (optical Doppler tomography) and tumor proliferation (green fluorescent protein [GFP] fluorescence). Microangiography demonstrates superior sensitivity (77.7 ± 6.4%) and specificity (98.2 ± 1.7%) over other imaging technologies (eg, optical coherence tomography) for tumor margin detection. Additionally, we report longitudinal in vivo imaging of tumor progression and show that the abrupt tumor cell proliferation did not occur until local capillary density and cerebral blood flow reached their peak approximately 2 weeks after tumor implantation. The unique capability of longitudinal multimodality imaging of tumor angiogenesis may provide new insights in tumor biology and in vivo assessment of the treatment effects on anti‐angiogenesis therapy for brain cancer.     

Remission spectrometry for blood vessel detection during stereotactic biopsy of brain tumors

Stereotactic biopsy is used to enable diagnostic confirmation of brain tumors and treatment planning. Despite being a well‐established technique, it is related to significant morbidity and mortality rates mostly caused by hemorrhages due to blood vessel ruptures. This paper presents a method of vessel detection during stereotactic biopsy that can be easily implemented by integrating two side‐view fibers into a conventional side‐cutting biopsy needle. Tissue within the needle window is illuminated through the first fiber; the second fiber detects the remitted light. By taking the ratio of the intensities at two wavelengths with strongly differing hemoglobin absorption, blood vessels can be recognized immediately before biopsy sampling. Via ray tracing simulations and phantom experiments, the dependency of the remission ratio R = I₅₇₈/I₆₅₀ on various parameters (blood oxygenation, fiber‐to‐vessel and inter‐fiber distance, vessel diameter and orientation) was investigated for a bare‐fiber probe. Up to 800–1200 µm away from the probe, a vessel can be recognized by a considerable reduction of the remission ratio from the background level. The technique was also successfully tested with a real biopsy needle probe on both optical phantoms and ex‐vivo porcine brain tissue, thus showing potential to improve the safety of stereotactic biopsy.

Dual‐wavelength remission measurement for the detection of blood vessels during stereotactic biopsy.     

Nanoplasmonic sensor for foodborne pathogens detection. Towards development of ISO‐SERS methodology for taxonomic affiliation of Campylobacter spp.

According to EU summary report on zoonoses, zoonotic agents and food‐borne outbreaks in 2017, Campylobacter was the most commonly reported gastrointestinal bacterial pathogen in humans in the EU. Unfortunately, the standard methods for the detection of thermotolerant Campylobacter spp. in foods are time‐consuming. Additionally, the qualified staff is obligatory. For this reason, new methods of pathogens detection are needed. The present work demonstrates that surface‐enhanced Raman scattering (SERS) is a reliable and fast method for detection of Campylobacter spp. in food samples. The proposed method combines the SERS measurements performed on an Ag/Si substrate with two initial steps of the ISO standard procedure. Finally, the principal component analysis (PCA) allows for statistical classification of the studied bacteria. By applying the proposed ISO‐SERS‐PCA method in the case of Campylobacter bacteria the total detection time may be reduced from 7 to 8 days required by ISO method to 3 to 4 days in the case of SERS‐based approach.     

Convolutional neural network‐based malaria diagnosis from focus stack of blood smear images acquired using custom‐built slide scanner

The present paper introduces a focus stacking‐based approach for automated quantitative detection of Plasmodium falciparum malaria from blood smear. For the detection, a custom designed convolutional neural network (CNN) operating on focus stack of images is used. The cell counting problem is addressed as the segmentation problem and we propose a 2‐level segmentation strategy. Use of CNN operating on focus stack for the detection of malaria is first of its kind, and it not only improved the detection accuracy (both in terms of sensitivity [97.06%] and specificity [98.50%]) but also favored the processing on cell patches and avoided the need for hand‐engineered features. The slide images are acquired with a custom‐built portable slide scanner made from low‐cost, off‐the‐shelf components and is suitable for point‐of‐care diagnostics. The proposed approach of employing sophisticated algorithmic processing together with inexpensive instrumentation can potentially benefit clinicians to enable malaria diagnosis.      

Indocyanine green-enhanced multimodal photoacoustic microscopy and optical coherence tomography molecular imaging of choroidal neovascularization

Photoacoustic microscopy (PAM) has great potential for visualization of the microvasculature with high spatial resolution and contrast. Early detection and differentiation of newly developed blood vessels named choroidal neovascularization (CNV) from normal vasculature remains a challenge in ophthalmology. Exogenous contrast agents can assist with improving PAM sensitivity, leading to differentiation of CNV. Here, an FDA-approved indocyanine green (ICG) was utilized as a PAM contrast agent. ICG was conjugated with RGD peptides, allowing the ICG to bind to the integrin expressed in CNV. Molecular PAM imaging showed that ICG-RGD can target CNV for up to 5 days post intravenous administration in living rabbits with a model of CNV. The PAM image sensitivity and image contrast were significantly enhanced by 15-fold at 24 h post-injection. Overall, the presented approach demonstrates the possibility of targeted ICG to be employed in PAM molecular imaging, allowing more precise evaluation of neovascularization.     

Automatic greenhouse insect pest detection and recognition based on a cascaded deep learning classification method

Inspection of insect sticky paper traps is an essential task for an effective integrated pest management (IPM) programme. However, identification and counting of the insect pests stuck on the traps is a very cumbersome task. Therefore, an efficient approach is needed to alleviate the problem and to provide timely information on insect pests. In this research, an automatic method for the multi-class recognition of small-size greenhouse insect pests on sticky paper trap images acquired by wireless imaging devices is proposed. The developed algorithm features a cascaded approach that uses a convolutional neural network (CNN) object detector and CNN image classifiers, separately. The object detector was trained for detecting objects in an image, and a CNN classifier was applied to further filter out non-insect objects from the detected objects in the first stage. The obtained insect objects were then further classified into flies (Diptera: Drosophilidae), gnats (Diptera: Sciaridae), thrips (Thysanoptera: Thripidae) and whiteflies (Hemiptera: Aleyrodidae), using a multi-class CNN classifier in the second stage. Advantages of this approach include flexibility in adding more classes to the multi-class insect classifier and sample control strategies to improve classification performance. The algorithm was developed and tested for images taken by multiple wireless imaging devices installed in several greenhouses under natural and variable lighting environments. Based on the testing results from long-term experiments in greenhouses, it was found that the algorithm could achieve average F₁-scores of 0.92 and 0.90 and mean counting accuracies of 0.91 and 0.90, as tested on a separate 6-month image data set and on an image data set from a different greenhouse, respectively. The proposed method in this research resolves important problems for the automated recognition of insect pests and provides instantaneous information of insect pest occurrences in greenhouses, which offers vast potential for developing more efficient IPM strategies in agriculture.     

Sentinel lymph node detection by combining nonradioactive techniques with contrast agents: State of the art and prospects

The status of sentinel lymph nodes (SLNs) has a substantial prognostic value because these nodes are the first place where cancer cells accumulate along their spreading route. Routine SLN biopsy (“gold standard”) involves peritumoral injections of radiopharmaceuticals, such as technetium-99m, which has obvious disadvantages. This review examines the methods used as “gold standard” analogs to diagnose SLNs. Nonradioactive preoperative and intraoperative methods of SLN detection are analyzed. Promising photonic tools for SLNs detection are reviewed, including NIR-I/NIR-II fluorescence imaging, photoswitching dyes for SLN detection, in vivo photoacoustic detection, imaging and biopsy of SLNs. Also are discussed methods of SLN detection by magnetic resonance imaging, ultrasonic imaging systems including as combined with photoacoustic imaging, and methods based on the magnetometer-aided detection of superparamagnetic nanoparticles. The advantages and disadvantages of nonradioactive SLN-detection methods are shown. The review concludes with prospects for the use of conservative diagnostic methods in combination with photonic tools.     

Autofluorescence imaging for recurrence detection in skin cancer postoperative scars

This clinical study is a first attempt to use autofluorescence for recurrence diagnosis of skin cancer in postoperative scars. The proposed diagnostic parameter is based on a reduction in scar autofluorescence, evaluated in the green spectral channel. The validity of the method has been tested on 110 postoperative scars from 56 patients suspected of non‐melanoma skin cancer, with eight patients (13 scars) available for the repeated examination. The recurrence diagnosis within a scar has been made after two subsequent autofluorescence check‐ups, representing the temporal difference between the scar autofluorescence amplitudes as a vector. The recognition of recurrence has been discussed to represent the significant deviations from the value of vector angle θ. This new autofluorescence‐based method can be easily integrated into the postoperative monitoring of surgical scars and can help diagnose the recurrence of skin cancer from the early stage of scar development.     

Label‐free optical detection of cyclosporine in biological fluids

In this paper, a spectroscopic method for determination of cyclosporine concentrations in biological fluids is presented. Blood plasma and hemoglobin solutions are chosen for the experiment. For various cyclosporine concentrations in blood plasma and hemoglobin, absorbance measurements in spectra range from 600 to 1100 nm are performed. The measurement results are analyzed by the use of a dedicated algorithm. The obtained data are characterized by a high coefficient of correlation R², which is equal to 0.9461 and 0.9808 for blood plasma and hemoglobin, respectively. The proposed method enables the selective detection of cyclosporine level and could be applied in medicine and laboratory diagnostics. The obtained result can be the base to build the point‐of‐care CsA level detection optical sensor.      

YoloXT: A object detection algorithm for marine benthos

In recent years, the marine economy has developed rapidly, and human demand for marine resources has increased greatly. At present, target detection technology has a wide range of applications and prospects in seabed observation and ocean engineering. However, the accuracy and robustness of existing target detection methods are low due to the complex underwater environment, poor lighting, and poor quality of undersea images and videos. To solve these problems, this paper proposes YoloXT, a new quantitative identification method for marine benthos. YoloXT introduces the DECA (Deformable Coordinate Attention) module, which expands the spatial awareness in feature extraction and can learn image features more effectively. Meanwhile FPST-PAN (Feature Pyramid S2win Transformer, Improved Path Aggregation Network) was proposed to deal with the problem of marine benthic target diversity. It further integrates deep and shallow features through multi-scale skip-connection and Transformer and improves the model's ability to deal with complex and changeable marine environments. Finally, the positive and negative sample assignment strategy OAA (Optimal Anchor Assignment) applied to the detection head is proposed. It effectively avoids the problem of unbalanced distribution of positive and negative samples caused by traditional sample assignment methods and marine benthos image noise. Experiments on the IOC-URPC dataset show that the mAP of YoloXT is 3.9% higher than that of YoloX, reaching 70.9%. YoloXT has demonstrated excellent performance in quantitative identification task of marine organisms, which can effectively contribute to the exploitation and conservation of marine resources. The source code is publicly available at https://github.com/F1veZhang/YOLOXT.     

Optimization for continuous-wave terahertz reflection imaging for biological tissues

Continuous-wave terahertz reflection imaging is a potential tool for biological tissues. Based on our home-made continuous-wave terahertz reflection imaging system, the effect of both polarization mode and reflection window on the imaging performance is studied theoretically and experimentally, showing good agreement. By taking obtaining sample information and image contrast into consideration, p-polarized terahertz waves are recommended. Moreover, considering the sample boundary identification and the image contrast, selection criteria for reflection window are proposed. This work will help to improve the performance of continuous-wave terahertz reflection imaging and accelerate the THz imaging in biological application.     

Inside Front Cover: Fast and sensitive delineation of brain tumor with clinically compatible moxifloxacin labeling and confocal microscopy (J. Biophotonics 1/2020)

A new high‐speed cellular imaging method using clinically applicable moxifloxacin labeling, called as moxifloxacin based confocal microscopy (MBCM), was developed for fast and sensitive tumor detection and delineation. The MBCM demarcated malignant brain tumor from normal brain by visualizing dense and irregular cell distribution in the tumor. An image processing algorithm was developed for automated brain tumor detection. Further details can be found in the article by Seunghun Lee, Won Yeong Park, Hoonchul Chang, et al. ( e201900197 ).

     

Estimating the decision curve and its precision from three study designs

The decision curve plots the net benefit of a risk model for making decisions over a range of risk thresholds, corresponding to different ratios of misclassification costs. We discuss three methods to estimate the decision curve, together with corresponding methods of inference and methods to compare two risk models at a given risk threshold. One method uses risks (R) and a binary event indicator (Y) on the entire validation cohort. This method makes no assumptions on how well-calibrated the risk model is nor on the incidence of disease in the population and is comparatively robust to model miscalibration. If one assumes that the model is well-calibrated, one can compute a much more precise estimate of based on risks R alone. However, if the risk model is miscalibrated, serious bias can result. Case–control data can also be used to estimate if the incidence (or prevalence) of the event () is known. This strategy has comparable efficiency to using the full data, and its efficiency is only modestly less than that for the full data if the incidence is estimated from the mean of Y. We estimate variances using influence functions and propose a bootstrap procedure to obtain simultaneous confidence bands around the decision curve for a range of thresholds. The influence function approach to estimate variances can also be applied to cohorts derived from complex survey samples instead of simple random samples.     

Classifying T cell activity in autofluorescence intensity images with convolutional neural networks

The importance of T cells in immunotherapy has motivated developing technologies to improve therapeutic efficacy. One objective is assessing antigen‐induced T cell activation because only functionally active T cells are capable of killing the desired targets. Autofluorescence imaging can distinguish T cell activity states in a non‐destructive manner by detecting endogenous changes in metabolic co‐enzymes such as NAD(P)H. However, recognizing robust activity patterns is computationally challenging in the absence of exogenous labels. We demonstrate machine learning methods that can accurately classify T cell activity across human donors from NAD(P)H intensity images. Using 8260 cropped single‐cell images from six donors, we evaluate classifiers ranging from traditional models that use previously‐extracted image features to convolutional neural networks (CNNs) pre‐trained on general non‐biological images. Adapting pre‐trained CNNs for the T cell activity classification task provides substantially better performance than traditional models or a simple CNN trained with the autofluorescence images alone. Visualizing the images with dimension reduction provides intuition into why the CNNs achieve higher accuracy than other approaches. Our image processing and classifier training software is available at https://github.com/gitter‐lab/t‐cell‐classification .     

Automated location invariant animal detection in camera trap images using publicly available data sources

1. A time‐consuming challenge faced by camera trap practitioners is the extraction of meaningful data from images to inform ecological management. An increasingly popular solution is automated image classification software. However, most solutions are not sufficiently robust to be deployed on a large scale due to lack of location invariance when transferring models between sites. This prevents optimal use of ecological data resulting in significant expenditure of time and resources to annotate and retrain deep learning models.
2. We present a method ecologists can use to develop optimized location invariant camera trap object detectors by (a) evaluating publicly available image datasets characterized by high intradataset variability in training deep learning models for camera trap object detection and (b) using small subsets of camera trap images to optimize models for high accuracy domain‐specific applications.
3. We collected and annotated three datasets of images of striped hyena, rhinoceros, and pigs, from the image‐sharing websites FlickR and iNaturalist (FiN), to train three object detection models. We compared the performance of these models to that of three models trained on the Wildlife Conservation Society and Camera CATalogue datasets, when tested on out‐of‐sample Snapshot Serengeti datasets. We then increased FiN model robustness by infusing small subsets of camera trap images into training.
4. In all experiments, the mean Average Precision (mAP) of the FiN trained models was significantly higher (82.33%–88.59%) than that achieved by the models trained only on camera trap datasets (38.5%–66.74%). Infusion further improved mAP by 1.78%–32.08%.
5. Ecologists can use FiN images for training deep learning object detection solutions for camera trap image processing to develop location invariant, robust, out‐of‐the‐box software. Models can be further optimized by infusion of 5%–10% camera trap images into training data. This would allow AI technologies to be deployed on a large scale in ecological applications. Datasets and code related to this study are open source and available on this repository: https://doi.org/10.5061/dryad.1c59zw3tx.

     

Label-free optical antibody testing kit based on a self-assembled whispering-gallery-mode microsphere

The appearance of antibodies in blood is a critical signal to suggest the infection. A rapid and accurate detection method for the antibody is significant to the disease diagnosis, especially for the epidemic. To this end, a highly sensitive whispering-gallery-mode (WGM) optical testing kit is designed and fabricated for detecting the specific immunoglobulin antibodies. The key component of the kit is a silica self-assembled microsphere decorated with the nucleocapsid proteins (N-proteins) of the SARS-CoV-2 virus. After the N-protein antibody immunoglobulin G (N-IgG) and immunoglobulin M (N-IgM) solutions being injected into the kit, the WGM red-shifts due to the antigen–antibody reaction. The wavelength displacement rates are proportional to the concentrations of these two antibodies from 1 to 100 μg/mL. A good specificity of the kit is demonstrated by the nonspecific human immunoglobulin G (H-IgG) and immunoglobulin M (H-IgM).     

Noninvasive detection of nanoscale structural changes in cornea associated with cross‐linking treatment

Corneal cross‐linking (CXL) using ultraviolet‐A (UVA) irradiation with a riboflavin photosensitizer has grown from an interesting concept to a practical clinical treatment for corneal ectatic diseases globally, such as keratoconus. To characterize the corneal structural changes, existing methods such as X‐ray microscopy, transmission electron microscopy, histology and optical coherence tomography (OCT) have been used. However, these methods have various drawbacks such as invasive detection, the impossibility for in vivo measurement, or limited resolution and sensitivity to structural alterations. Here, we report the application of oversampling nanosensitive OCT for probing the corneal structural alterations. The results indicate that the spatial period increases slightly after 30 minutes riboflavin instillation but decreases significantly after 30 minutes UVA irradiation following the Dresden protocol. The proposed noninvasive method can be implemented using existing OCT systems, without any additional components, for detecting nanoscale changes with the potential to assist diagnostic assessment during CXL treatment, and possibly to be a real‐time monitoring tool in clinics.