AIGO-DTI: Predicting Drug-Target Interactions Based on Improved Drug Properties Combined with Adaptive Iterative Algorithms.

Artificial intelligence-based methods for predicting drug-target interactions (DTIs) aim to explore reliable drug candidate targets rapidly and cost-effectively to accelerate the drug development process. However, current methods are often limited by the topological regularities of drug molecules, making them difficult to generalize to a broader chemical space. Additionally, the use of similarity to measure DTI network links often introduces noise, leading to false DTI relationships and affecting the prediction accuracy. To address these issues, this study proposes an Adaptive Iterative Graph Optimization (AIGO)-DTI prediction framework. This framework integrates atomic cluster information and enhances molecular features through the design of functional group prompts and graph encoders, optimizing the construction of DTI association networks. Furthermore, the optimization of graph structure is transformed into a node similarity learning problem, utilizing multihead similarity metric functions to iteratively update the network structure to improve the quality of DTI information. Experimental results demonstrate the outstanding performance of AIGO-DTI on multiple public data sets and label reversal data sets. Case studies, molecular docking, and existing research validate its effectiveness and reliability. Overall, the method proposed in this study can construct comprehensive and reliable DTI association network information, providing new graphing and optimization strategies for DTI prediction, which contribute to efficient drug development and reduce target discovery costs.

High Selectivity Cofactor NADH Regeneration Organic Iridium Complexes Used for High-Efficiency Chem-Enzyme Cascade Catalytic Hydrogen Transfer.

Our research demonstrated that novel pentamethylcyclopentadienyl (Cp*) iridium pyridine sulfonamide complex PySO2NPh-Ir (7) could highly specifically catalyze nicotinamide adenine dinucleotide (NAD+) into the corresponding reducing cofactor NADH in cell growth media containing various biomolecules. The structures and catalytic mechanism of 7 were studied by single-crystal X-ray, NMR, electrochemical, and kinetic methods, and the formation of iridium hydride species Ir-H was confirmed to be the plausible hydride-transfer intermediate of 7. Moreover, benefiting from its high hydrogen-transfer activity and selectivity for NADH regeneration, 7 was used as an optimal metal catalyst to establish a chem-enzyme cascade catalytic hydrogen-transfer system, which realized the high-efficiency preparation of l-glutamic acid by combining with l-glutamate dehydrogenase (GLDH).

Electrochemiluminescent sensor based on an aggregation-induced emission probe for bioanalytical detection.

In recent years, with the rapid development of electrochemiluminescence (ECL) sensors, more luminophores have been designed to achieve high-throughput and reliable analysis. Impressively, after the proposed fantastic concept of "aggregation-induced electrochemiluminescence (AIECL)" by Cola, the application of AIECL emitters provides more abundant choices for the further improvement of ECL sensors. In this review, we briefly report the phenomenon, principle and representative applications of aggregation-induced emission (AIE) and AIECL emitters. Moreover, it is noteworthy that the cases of AIECL sensors for bioanalytical detection are summarized in detail, from 2017 to now. Finally, inspired by the applications of AIECL emitters, relevant prospects and challenges for AIECL sensors are proposed, which is of great significance for exploring more advanced bioanalytical detection technology.

An efficient aggregation-induced electrochemiluminescent immunosensor by using TiO2 nanoparticles as coreaction accelerator and energy donor for aflatoxin B1 detection.

Herein, we fabricated a label-free ECL immunosensor for aflatoxin B1 (AFB1) detection. In this system, a small organic aggregation-induced electrochemiluminescence luminophore, 2,5-di-tetraphenylethylene-ylthiazolo [5,4-d] thiazole, was designed, named TPETTZ. Polyaniline-wrapped TiO2 nanoparticles (PANI/TiO2 NPs) complex was synthesized through one-step in situ oxidation polymerization of aniline, and performed excellent electrical conductivity and abundant amino groups. As an ECL accelerator, TiO2 nanoparticles (TiO2 NPs) promoted the oxidation of tri-n-propylamine (TPA) to generate more TPA•; in addition, it also acted as a donor to improve the ECL intensity of TPETTZ (acceptor) through electrochemiluminescence resonance energy transfer (ECL-RET). Encouraged by the above, under the existence of TPA, TPETTZ displayed a strong and continuously stable ECLanode signal due to the introduction of PANI/TiO2 NPs. Therefore, the immunosensor was constructed for AFB1 detection based on the quenching effect of target on the ECL signal, and a linearly decreasing ECL signal was obtained as the increasement of AFB1 in the range of 75 fg/mL to 100 ng/mL, with a lower detection limit of 27.5 fg/mL. Moreover, the as-prepared sensing platform performed a satisfactory anti-interference, stability, and reproducibility, and appeared a good accuracy in walnut sample analysis, presenting a promising application in the future.

PrGeFNE: Predicting disease-related genes by fast network embedding.

Identifying disease-related genes is of importance for understanding of molecule mechanisms of diseases, as well as diagnosis and treatment of diseases. Many computational methods have been proposed to predict disease-related genes, but how to make full use of multi-source biological data to enhance the ability of disease-gene prediction is still challenging. In this paper, we proposed a novel method for predicting disease-related genes by using fast network embedding (PrGeFNE), which can integrate multiple types of associations related to diseases and genes. Specifically, we first constructed a heterogeneous network by using phenotype-disease, disease-gene, protein-protein and gene-GO associations; and low-dimensional representation of nodes is extracted from the network by using a fast network embedding algorithm. Then, a dual-layer heterogeneous network was reconstructed by using the low-dimensional representation, and a network propagation was applied to the dual-layer heterogeneous network to predict disease-related genes. Through cross-validation and newly added-association validation, we displayed the important roles of different types of association data in enhancing the ability of disease-gene prediction, and confirmed the excellent performance of PrGeFNE by comparing to state-of-the-art algorithms. Furthermore, we developed a web tool that can facilitate researchers to search for candidate genes of different diseases predicted by PrGeFNE, along with the enrichment analysis of GO and pathway on candidate gene set. This may be useful for investigation of diseases' molecular mechanisms as well as their experimental validations. The web tool is available at http://bioinformatics.csu.edu.cn/prgefne/.

An ultrasensitive ratiometric immunosensor based on the ratios of conjugated distyrylbenzene derivative nanosheets with AIECL properties and electrochemical signal for CYFRA21-1 detection.

Aggregation-induced electrochemiluminescence reagent, a distyrylbenzene derivative with donor-acceptor conjugated nanosheet structure, namely TPAPCN, was used as a trace label and modified on the electrode through the formation of classical sandwich complex of antibody-antigen-antibody in this work. In aggregate state, TPAPCN with twisted structure was limited in nanometer space through intermolecular π - π stacking interactions, which not only restricts the intramolecular motions but also combines a large number of singlet excitons to greatly trigger electrochemiluminescence (ECL). The ECL signal of this system enhanced with more captured cytokeratin 19 fragment 21-1 (CYFRA21-1) on the modified electrode. Three-dimensional graphene/platinum nanoparticles with large specific surface, and excellent electroconductivity and biocompatibility were prepared and acted as excellent carriers for thionine handling (3D-GN/PtNPs/Th), which was employed for improving the loading of antibodies and generating internal electrochemical signal. Consequently, a novel ratiometric sandwich immunosensor for CYFRA21-1 detection was fabricated based on TPAPCN and 3D-GN/PtNPs/Th, that is, a rapid and reliable detection was achieved through the ratio between ECL and electrochemical signals. The prepared sensor performed good linearity in the range of 50 fg/mL to 1 ng/mL with a detection limit as low as 16 fg/mL. Moreover, the detection results revealed well in the analysis of human serum samples, demonstrating a significant application for clinical monitoring and biomolecules detection.

Classification of multi-differentiated liver cancer pathological images based on deep learning attention mechanism.

PURPOSE: Liver cancer is one of the most common malignant tumors in the world, ranking fifth in malignant tumors. The degree of differentiation can reflect the degree of malignancy. The degree of malignancy of liver cancer can be divided into three types: poorly differentiated, moderately differentiated, and well differentiated. Diagnosis and treatment of different levels of differentiation are crucial to the survival rate and survival time of patients. As the gold standard for liver cancer diagnosis, histopathological images can accurately distinguish liver cancers of different levels of differentiation. Therefore, the study of intelligent classification of histopathological images is of great significance to patients with liver cancer. At present, the classification of histopathological images of liver cancer with different degrees of differentiation has disadvantages such as time-consuming, labor-intensive, and large manual investment. In this context, the importance of intelligent classification of histopathological images is obvious. METHODS: Based on the development of a complete data acquisition scheme, this paper applies the SENet deep learning model to the intelligent classification of all types of differentiated liver cancer histopathological images for the first time, and compares it with the four deep learning models of VGG16, ResNet50, ResNet_CBAM, and SKNet. The evaluation indexes adopted in this paper include confusion matrix, Precision, recall, F1 Score, etc. These evaluation indexes can be used to evaluate the model in a very comprehensive and accurate way. RESULTS: Five different deep learning classification models are applied to collect the data set and evaluate model. The experimental results show that the SENet model has achieved the best classification effect with an accuracy of 95.27%. The model also has good reliability and generalization ability. The experiment proves that the SENet deep learning model has a good application prospect in the intelligent classification of histopathological images. CONCLUSIONS: This study also proves that deep learning has great application value in solving the time-consuming and laborious problems existing in traditional manual film reading, and it has certain practical significance for the intelligent classification research of other cancer histopathological images.

The application of feature engineering in establishing a rapid and robust model for identifying patients with glioma.

The aim of the study is to evaluate the efficacy of the combination of Raman spectroscopy with feature engineering and machine learning algorithms for detecting glioma patients. In this study, we used Raman spectroscopy technology to collect serum spectra of glioma patients and healthy people and used feature engineering-based classification models for prediction. First, to reduce the dimensionality of the data, we used two feature extraction algorithms which are partial least squares (PLS) and principal component analysis (PCA). Then, the principal components were selected using the feature selection methods of four correlation indexes, namely, Relief-F (RF), the Pearson correlation coefficient (PCC), the F-score (FS) and term variance (TV). Finally, back-propagation neural network (BP), linear discriminant analysis (LDA) and support vector machine (SVM) classification models were established. To improve the reliability of the model, we used a fivefold cross validation to measure the prediction performance between different models. In this experiment, 33 classification models were established. Integrating 4 classification criteria, PLS-Relief-F-BP, PLS-F-Score-BP, PLS-LDA and PLS-Relief-F-SVM had better effects, and their accuracy rates reached 97.58%, 96.33%, 97.87% and 96.19%, respectively. The experimental results show that feature engineering can select more representative features, reduce computational time complexity and simplify the model. The classification model established in this experiment can not only increase the robustness of the model and shorten the discrimination time but also realize the rapid, stable and accurate diagnosis of glioma patients, which has high clinical application value.

Rapid noninvasive screening of cerebral ischemia and cerebral infarction based on tear Raman spectroscopy combined with multiple machine learning algorithms.

Researchers have established a classification model based on tear Raman spectroscopy combined with machine learning classification algorithms, which realizes rapid noninvasive classification of cerebral infarction and cerebral ischemia, which is of great significance for clinical medical diagnosis. Through spectral data analysis, it is found that there are differences in the content of tyrosine, phenylalanine, and carotenoids in the tears of patients with cerebral ischemia and patients with cerebral infarction. We try to establish a classification model for rapid noninvasive screening of cerebral infarction and cerebral ischemia through these differences. The experiment has four parts, including normalization, data enhancement, feature extraction, and data classification. The researchers combined three feature extraction methods with four machine classification models to build a total of 12 classification models. Integrating 8 classification criteria, the classification accuracy of all models is above 85%, especially PLS-PNN has achieved 100% accuracy and better running time. The experimental results show that tear Raman spectroscopy combined with machine learning classification model has a good effect on the screening of cerebral ischemia and cerebral infarction, which is conducive to the noninvasive and rapid clinical diagnosis of cerebrovascular diseases in the future.

Detection of ß-Lactoglobulin by a Porous Silicon Microcavity Biosensor Based on the Angle Spectrum.

In this paper, carbon quantum dot-labelled ß-lactoglobulin antibodies were used for refractive index magnification, and ß-lactoglobulin was detected by angle spectroscopy. In this method, the detection light is provided by a He-Ne laser whose central wavelength is the same as that of the porous silicon microcavity device, and the light source was changed to a parallel beam to illuminate the porous silicon microcavity' surface by collimating beam expansion, and the reflected light was received on the porous silicon microcavity' surface by a detector. The angle corresponding to the smallest luminous intensity before and after the onset of immune response was measured by a detector for different concentrations of ß-lactoglobulin antigen and carbon quantum dot-labelled ß-lactoglobulin antibodies, and the relationship between the variation in angle before and after the immune response was obtained for different concentrations of the ß-lactoglobulin antigen. The results of the experiment present that the angle variations changed linearly with increasing ß-lactoglobulin antigen concentration before and after the immune response. The limit of detection of ß-lactoglobulin by this method was 0.73 µg/L, indicating that the method can be used to detect ß-lactoglobulin quickly and conveniently at low cost.

Spectral-Free Double Light Detection of DNA Based on a Porous Silicon Bragg Mirror.

To improve the detection sensitivity of a porous silicon optical biosensor in the real-time detection of biomolecules, a non-spectral porous silicon optical biosensor technology, based on dual-signal light detection, is proposed. Double-light detection is a combination of refractive index change detection and fluorescence change detection. It uses quantum dots to label probe molecules to detect target molecules. In the double-signal-light detection method, the first detection-signal light is the detection light that is reflected from the surface of the porous silicon Bragg mirror. The wavelength of the detection light is the same as the wavelength of the photonic band gap edge of the porous silicon Bragg mirror. CdSe/ZnS quantum dots are used to label the probe DNA and hybridize it with the target DNA molecules in the pores of porous silicon to improve its effective refractive index and enhance the detection-reflection light. The second detection-signal light is fluorescence, which is generated by the quantum dots in the reactant that are excited by light of a certain wavelength. The Bragg mirror structure further enhances the fluorescence signal. A digital microscope is used to simultaneously receive the digital image of two kinds of signal light superimposed on the surface of porous silicon, and the corresponding algorithm is used to calculate the change in the average grey value before and after the hybridization reaction to calculate the concentration of the DNA molecules. The detection limit of the DNA molecules was 0.42 pM. This method can not only detect target DNA by hybridization, but also detect antigen by immune reaction or parallel biochip detection for a porous silicon biosensor.

Construction of polythiophene-derivative films as a novel electrochemical sensor for highly sensitive detection of nitrite.

Herein, a novel, convenient, and highly selective electrochemical sensor for determination of nitrite based on a polythiophene-derivative film-modified glassy carbon electrode (GCE) was established. In this work, 2,5-di-thiophen-3-yl-thiazolo[5,4-d]thiazole (DTT), a novel thiophene derivative, was synthesized and used to form an original and excellent polymer film (PolyDTTF) on GCE through one-step electropolymerization for the first time. The modified electrodes were characterized by electron microscopy (SEM), Fourier transform infra-red spectroscopy (FT-IR), UV-visible spectra, Raman spectroscopy, and electrochemical technologies, in which the electrochemical sensor based on PolyDTTF was successfully constructed and demonstrated a significant electrocatalytic effect on nitrite. The influence of pH value, electrodeposition scanning times, scanning speed, and potential on the electrochemical behavior of nitrite were investigated in detail. Furthermore, the nitrite sensor exhibits excellent responses proportional to nitrite concentrations (R2 = 0.9972) over a concentration range of 5.5 × 10-9 ~ 3.5 × 10-5 M with a detection limit (LOD) of 2 nM, and has extremely good anti-interference ability for nitrite detection. This proposed sensor can be used to detect nitrite in actual samples, opening the possibility for applications in the food industry and environmental analysis.

Prediction of tumor size in patients with invasive ductal carcinoma using FT-IR spectroscopy combined with chemometrics: a preliminary study.

Precise detection of tumor size is essential for early diagnosis, treatment, and evaluation of the prognosis of breast cancer. However, there are some errors between the tumor size of breast cancer measured by conventional imaging methods and the pathological tumor size. Invasive ductal carcinoma (IDC) is a common pathological type of breast cancer. In this study, serum Fourier transform infrared spectroscopy (FT-IR) combined with chemometric methods was used to predict the maximum diameter and maximum vertical diameter of tumors in IDC patients. Three models were evaluated based on the pathological tumor size measured after surgery and included grid search support vector machine regression (GS-SVR), back propagation neural network optimized by genetic algorithm (GA-BP-ANN), and back propagation neural network optimized by particle swarm optimization (PSO-BP-ANN). The results show that three models can accurately predict tumor size. The GA-BP-ANN model provided the best fitting quality of the largest tumor diameter with the determination coefficients of 0.984 in test set. And the GS-SVR model provided the best fitting quality of the largest vertical tumor diameter with the determination coefficients of 0.982 in test set. The GS-SVR model had the highest prediction efficiency and the lowest time complexity of the models. The results indicate that serum FT-IR spectroscopy combined with chemometric methods can predict tumor size in IDC patients. In addition, compared with traditional imaging methods, we found that the experimental results of the three models are better than traditional imaging methods in terms of correlation and fitting degree. And the average fitting error of PSO-BP-ANN and GA-BP-ANN models was less than 0.3 mm. The minimally invasive detection method is expected to be developed into a new clinical diagnostic method for tumor size estimation to reduce the diagnostic trauma of patients and provide new diagnostic experience for patients. Graphical Abstract.

Raman spectroscopic study of cervical precancerous lesions and cervical cancer.

Early detection of cervical lesions, accurate diagnosis of cervical lesions, and timely and effective therapy can effectively avoid the occurrence of cervical cancer or improve the survival rate of patients. In this paper, the spectra of tissue sections of cervical inflammation (n = 60), CIN (cervical intraepithelial neoplasia) I (n = 30), CIN II (n = 30), CIN III (n = 30), cervical squamous cell carcinoma (n = 30), and cervical adenocarcinoma (n = 30) were collected by a confocal Raman micro-spectrometer (LabRAM HR Evolution, Horiba France SAS, Villeneuve d'Ascq, France). The Raman spectra of six kinds of cervical tissues were analyzed, the dominant Raman peaks of different kinds of tissues were summarized, and the differences in chemical composition between the six tissue samples were compared. An independent sample t test (p ≤ 0.05) was used to analyze the difference of average relative intensity of Raman spectra of six types of cervical tissues. The difference of relative intensity of Raman spectra of six kinds of tissues can reflect the difference of biochemical components in six kinds of tissues and the characteristic of biochemical components in different kinds of tissues. The classification models of cervical inflammation, CIN I, CIN II, CIN III, cervical squamous cell carcinoma, and cervical adenocarcinoma were established by using a support vector machine (SVM) algorithm. Six types of cervical tissues were classified and identified with an overall diagnostic accuracy of 85.7%. This study laid a foundation for the application of Raman spectroscopy in the clinical diagnosis of cervical precancerous lesions and cervical cancer.

Microwave method synthesis of magnetic ionic liquid/gold nanoparticles as ultrasensitive SERS substrates for trace clopidol detection.

Clopidol is one of the most widely used anti-coccidiosis drugs. Its residues in poultry products and the environment pose a serious threat to human health. In this work, microwave-assisted synthesis of magnetic ionic liquid/gold nanoparticles (MIL-Au NPs) as the SERS substrates were first designed for sensitive and reliable determination of clopidol residue in egg samples. The experiment shows that MIL(1-methyl-3-hexyl imidazole ferric tetrachloride ([C6mim]FeCl4)) and microwave play a key role in the dispersion and morphology of Au NPs. Under the optimal conditions, the as-prepared MIL-Au NPs were applied to the SERS detection of clopidol in methanol and egg solution and its detection limits can be as low as to 0.5 µg/kg (equal to 0.5 ppb) in both solutions. The standard curves with regression coefficients of 0.9298 and 0.93496 were constructed in the linear range of 100-1000 ppb and 0.5-50 ppb for clopidol in egg solutions. Moreover, satisfactory recoveries (97.5-103.2%) were obtained for egg samples. The developed SERS method provides a way for quantitation of clopidol and can be applied for the convenient, reliable, and highly sensitive detection of antibiotic residues in food and environment, which has great potential in food safety and biological monitoring. Graphical abstract.

Rapid screening of hepatitis B using Raman spectroscopy and long short-term memory neural network.

This study presents a rapid method to screen hepatitis B patients using serum Raman spectroscopy combined with long short-term memory neural network (LSTM). The serum samples taken from 435 hepatitis B patients and 699 non-hepatitis B people were measured in this experiment. Specific biomolecular changes in three groups of serum samples could be seen in the tentative assignment of Raman peaks. First, principal component analysis (PCA) was used for extracting key features of spectral data, which reduces the dimension of the multidimensional spectrum. Then, LSTM is used to train the spectral data. Finally, the full connection layer completes the classification of HBV. The diagnostic accuracy of the first LSTM model is 97.32%, and the value of AUC is 0.995. The results from the study demonstrate that the combination of serum Raman spectroscopy technique and LSTM provides an effective technical approach to the screening of hepatitis B.

AF-SENet: Classification of Cancer in Cervical Tissue Pathological Images Based on Fusing Deep Convolution Features.

Cervical cancer is the fourth most common cancer in the world. Whole-slide images (WSIs) are an important standard for the diagnosis of cervical cancer. Missed diagnoses and misdiagnoses often occur due to the high similarity in pathological cervical images, the large number of readings, the long reading time, and the insufficient experience levels of pathologists. Existing models have insufficient feature extraction and representation capabilities, and they suffer from insufficient pathological classification. Therefore, this work first designs an image processing algorithm for data augmentation. Second, the deep convolutional features are extracted by fine-tuning pre-trained deep network models, including ResNet50 v2, DenseNet121, Inception v3, VGGNet19, and Inception-ResNet, and then local binary patterns and a histogram of the oriented gradient to extract traditional image features are used. Third, the features extracted by the fine-tuned models are serially fused according to the feature representation ability parameters and the accuracy of multiple experiments proposed in this paper, and spectral embedding is used for dimension reduction. Finally, the fused features are inputted into the Analysis of Variance-F value-Spectral Embedding Net (AF-SENet) for classification. There are four different pathological images of the dataset: normal, low-grade squamous intraepithelial lesion (LSIL), high-grade squamous intraepithelial lesion (HSIL), and cancer. The dataset is divided into a training set (90%) and a test set (10%). The serial fusion effect of the deep features extracted by Resnet50v2 and DenseNet121 () is the best, with average classification accuracy reaching 95.33%, which is 1.07% higher than ResNet50 v2 and 1.05% higher than DenseNet121. The recognition ability is significantly improved, especially in LSIL, reaching 90.89%, which is 2.88% higher than ResNet50 v2 and 2.1% higher than DenseNet121. Thus, this method significantly improves the accuracy and generalization ability of pathological cervical WSI recognition by fusing deep features.

Recognition of Common Non-Normal Walking Actions Based on Relief-F Feature Selection and Relief-Bagging-SVM.

Action recognition algorithms are widely used in the fields of medical health and pedestrian dead reckoning (PDR). The classification and recognition of non-normal walking actions and normal walking actions are very important for improving the accuracy of medical health indicators and PDR steps. Existing motion recognition algorithms focus on the recognition of normal walking actions, and the recognition of non-normal walking actions common to daily life is incomplete or inaccurate, resulting in a low overall recognition accuracy. This paper proposes a microelectromechanical system (MEMS) action recognition method based on Relief-F feature selection and relief-bagging-support vector machine (SVM). Feature selection using the Relief-F algorithm reduces the dimensions by 16 and reduces the optimization time by an average of 9.55 s. Experiments show that the improved algorithm for identifying non-normal walking actions has an accuracy of 96.63%; compared with Decision Tree (DT), it increased by 11.63%; compared with k-nearest neighbor (KNN), it increased by 26.62%; and compared with random forest (RF), it increased by 11.63%. The average Area Under Curve (AUC) of the improved algorithm improved by 0.1143 compared to KNN, by 0.0235 compared to DT, and by 0.04 compared to RF.

The Enhanced Sensitivity of a Porous Silicon Microcavity Biosensor Based on an Angular Spectrum Using CdSe/ZnS Quantum Dots.

To improve the detection sensitivity of porous silicon microcavity biosensors, CdSe/ZnS quantum dots are used to label complementary DNA molecules for the refractive index amplification and angular spectrum method for detection. In this method, the TE mode laser is used as the detection light and the light source is changed into a parallel beam by collimating and expanding the beam, which illuminates the PSM surface and receives the reflected light from the PSM surface through the detector. The angle corresponding to the weakest reflected light intensity before and after the biological reaction between probe DNA and complementary DNA of different concentrations labeled by quantum dots was measured by the detector, and the relationship between the angle change before and after the biological reaction and the complementary DNA concentration labeled by quantum dots was obtained. The experimental results show that the angle change increases linearly with increasing complementary DNA concentration. The detection limit of the experiment, as determined by fitting, is approximately 36 pM. The detection limit of this method is approximately 1/300 of that without quantum dot labeling. Our method has a low cost because it does not require the use of a reflectance spectrometer, and it also demonstrates high sensitivity.

Computation and analysis of backward ray-tracing in aero-optics flow fields.

A backward ray-tracing method is proposed for aero-optics simulation. Different from forward tracing, the backward tracing direction is from the internal sensor to the distant target. Along this direction, the tracing in turn goes through the internal gas region, the aero-optics flow field, and the freestream. The coordinate value, the density, and the refractive index are calculated at each tracing step. A stopping criterion is developed to ensure the tracing stops at the outer edge of the aero-optics flow field. As a demonstration, the analysis is carried out for a typical blunt nosed vehicle. The backward tracing method and stopping criterion greatly simplify the ray-tracing computations in the aero-optics flow field, and they can be extended to our active laser illumination aero-optics study because of the reciprocity principle.