Research on Cyanobacterial-Bloom Detection Based on Multispectral Imaging and Deep-Learning Method.

Frequent outbreaks of cyanobacterial blooms have become one of the most challenging water ecosystem issues and a critical concern in environmental protection. To overcome the poor stability of traditional detection algorithms, this paper proposes a method for detecting cyanobacterial blooms based on a deep-learning algorithm. An improved vegetation-index method based on a multispectral image taken by an Unmanned Aerial Vehicle (UAV) was adopted to extract inconspicuous spectral features of cyanobacterial blooms. To enhance the recognition accuracy of cyanobacterial blooms in complex scenes with noise such as reflections and shadows, an improved transformer model based on a feature-enhancement module and pixel-correction fusion was employed. The algorithm proposed in this paper was implemented in several rivers in China, achieving a detection accuracy of cyanobacterial blooms of more than 85%. The estimate of the proportion of the algae bloom contamination area and the severity of pollution were basically accurate. This paper can lay a foundation for ecological and environmental departments for the effective prevention and control of cyanobacterial blooms.

High-risk Stage III colon cancer patients identified by a novel five-gene mutational signature are characterized by upregulation of IL-23A and gut bacterial translocation of the tumor microenvironment.

The heterogeneities of colorectal cancer (CRC) lead to staging inadequately of patients' prognosis. Here, we performed a prognostic analysis based on the tumor mutational profile and explored the characteristics of the high-risk tumors. We sequenced 338 colorectal carcinomas as the training dataset, constructed a novel five-gene (SMAD4, MUC16, COL6A3, FLG and LRP1B) prognostic signature, and validated it in an independent dataset from The Cancer Genome Atlas (TCGA). Kaplan-Meier and Cox regression analyses confirmed that the five-gene signature is an independent predictor of recurrence and prognosis in patients with Stage III colon cancer. The mutant signature translated to an increased risk of death (hazard ratio = 2.45, 95% confidence interval = 1.15-5.22, p = 0.016 in our dataset; hazard ratio = 4.78, 95% confidence interval = 1.33-17.16, p = 0.008 in TCGA dataset). RNA and bacterial 16S rRNA sequencing of high-risk tumors indicated that mutations of the five-gene signature may lead to intestinal barrier integrity, translocation of gut bacteria and deregulation of immune response and extracellular related genes. The high-risk tumors overexpressed IL23A and IL1RN genes and enriched with cancer-related bacteria (Bacteroides fragilis,Peptostreptococcus, Parvimonas, Alloprevotella and Gemella) compared to the low-risk tumors. The signature identified the high-risk group characterized by gut bacterial translocation and upregulation of interleukins of the tumor microenvironment, which was worth further researching.

Analysis and inspection techniques for mouse liver injury based on terahertz spectroscopy.

At present, researchers are exploring biological tissue detection method using terahertz techniques. In this paper, techniques to inspect mouse liver injury by using terahertz spectroscopy were studied. The boxplots were applied to remove abnormal data, and the maximal information coefficient was employed to select crucial features from the absorption coefficient and refractive index spectra. Random Forests and AdaBoost were applied to recognize different levels of liver injury. We found that AdaBoost had better performance on low-level injury classification. This work suggests that terahertz techniques have the potential to detect liver injury at an early stage and evaluate liver treatment strategies.

Application of Least-Squares Support Vector Machines for Quantitative Evaluation of Known Contaminant in Water Distribution System Using Online Water Quality Parameters.

In water-quality, early warning systems and qualitative detection of contaminants are always challenging. There are a number of parameters that need to be measured which are not entirely linearly related to pollutant concentrations. Besides the complex correlations between variable water parameters that need to be analyzed also impairs the accuracy of quantitative detection. In aspects of these problems, the application of least-squares support vector machines (LS-SVM) is used to evaluate the water contamination and various conventional water quality sensors quantitatively. The various contaminations may cause different correlative responses of sensors, and also the degree of response is related to the concentration of the injected contaminant. Therefore to enhance the reliability and accuracy of water contamination detection a new method is proposed. In this method, a new relative response parameter is introduced to calculate the differences between water quality parameters and their baselines. A variety of regression models has been examined, as result of its high performance, the regression model based on genetic algorithm (GA) is combined with LS-SVM. In this paper, the practical application of the proposed method is considered, controlled experiments are designed, and data is collected from the experimental setup. The measured data is applied to analyze the water contamination concentration. The evaluation of results validated that the LS-SVM model can adapt to the local nonlinear variations between water quality parameters and contamination concentration with the excellent generalization ability and accuracy. The validity of the proposed approach in concentration evaluation for potassium ferricyanide is proven to be more than 0.5 mg/L in water distribution systems.

In situ detection of water quality contamination events based on signal complexity analysis using online ultraviolet-visible spectral sensor.

The contaminant detection in water distribution systems is essential to protect public health from potentially harmful compounds resulting from accidental spills or intentional releases. As a noninvasive optical technique, ultraviolet-visible (UV-Vis) spectroscopy is investigated for detecting contamination events. However, current methods for event detection exhibit the shortcomings of noise susceptibility. In this paper, a new method that has less sensitivity to noise was proposed to detect water quality contamination events by analyzing the complexity of the UV-Vis spectrum series. The proposed method applied approximate entropy (ApEn) to measure spectrum signals' complexity, which made a distinction between normal and abnormal signals. The impact of noise was attenuated with the help of ApEn's insensitivity to signal disturbance. This method was tested on a real water distribution system data set with various concentration simulation events. Results from the experiment and analysis show that the proposed method has a good performance on noise tolerance and provides a better detection result compared with the autoregressive model and sequential probability ratio test.

Online Classification of Contaminants Based on Multi-Classification Support Vector Machine Using Conventional Water Quality Sensors.

Water quality early warning system is mainly used to detect deliberate or accidental water pollution events in water distribution systems. Identifying the types of pollutants is necessary after detecting the presence of pollutants to provide warning information about pollutant characteristics and emergency solutions. Thus, a real-time contaminant classification methodology, which uses the multi-classification support vector machine (SVM), is proposed in this study to obtain the probability for contaminants belonging to a category. The SVM-based model selected samples with indistinct feature, which were mostly low-concentration samples as the support vectors, thereby reducing the influence of the concentration of contaminants in the building process of a pattern library. The new sample points were classified into corresponding regions after constructing the classification boundaries with the support vector. Experimental results show that the multi-classification SVM-based approach is less affected by the concentration of contaminants when establishing a pattern library compared with the cosine distance classification method. Moreover, the proposed approach avoids making a single decision when classification features are unclear in the initial phase of injecting contaminants.

Distribution water quality anomaly detection from UV optical sensor monitoring data by integrating principal component analysis with chi-square distribution.

The issue of distribution water quality security ensuring is recently attracting global attention due to the potential threat from harmful contaminants. The real-time monitoring based on ultraviolet optical sensors is a promising technique. This method is of reagent-free, low maintenance cost, rapid analysis and wide cover range. However, the ultraviolet absorption spectra are of large size and easily interfered. While within the on-site application, there is almost no prior knowledge like spectral characteristics of potential contaminants before determined. Meanwhile, the concept of normal water quality is also varying due to the operating condition. In this paper, a procedure based on multivariate statistical analysis is proposed to detect distribution water quality anomaly based on ultraviolet optical sensors. Firstly, the principal component analysis is employed to capture the main variety features from the spectral matrix and reduce the dimensionality. A new statistical variable is then constructed and used for evaluating the local outlying degree according to the chi-square distribution in the principal component subspace. The possibility of anomaly of the latest observation is calculated by the accumulation of the outlying degrees from the adjacent previous observations. To develop a more reliable anomaly detection procedure, several key parameters are discussed. By utilizing the proposed methods, the distribution water quality anomalies and the optical abnormal changes can be detected. The contaminants intrusion experiment is conducted in a pilot-scale distribution system by injecting phenol solution. The effectiveness of the proposed procedure is finally testified using the experimental spectral data.

Microfluidic Channel-Based Soft Electrodes and Their Application in Capacitive Pressure Sensing.

Flexible and stretchable electrodes are essential components in soft artificial sensory systems. Despite recent advances in flexible electronics, most electrodes are either restricted by the patterning resolution or the capability of inkjet printing with high-viscosity super-elastic materials. In this paper, we present a simple strategy to fabricate microchannel-based stretchable composite electrodes, which can be achieved by scraping elastic conductive polymer composites (ECPCs) into lithographically embossed microfluidic channels. The ECPCs were prepared by a volatile solvent evaporation method, which achieves a uniform dispersion of carbon nanotubes (CNTs) in a polydimethylsiloxane (PDMS) matrix. Compared to conventional fabrication methods, the proposed technique can facilitate the rapid fabrication of well-defined stretchable electrodes with high-viscosity slurry. Since the electrodes in this work were made up of all-elastomeric materials, strong interlinks can be formed between the ECPCs-based electrodes and the PDMS-based substrate at the interfaces of the microchannel walls, which allows the electrodes to exhibit mechanical robustness under high tensile strains. In addition, the mechanical-electric response of the electrodes was also systematically studied. Finally, a soft pressure sensor was developed by combining a dielectric silicone foam and an interdigitated electrodes (IDE) layer, and this demonstrated great potential for pressure sensors in soft robotic tactile sensing applications.

Study on the detection method of biological characteristics of hepatoma cells based on terahertz time-domain spectroscopy.

Liver cancer usually has a high degree of malignancy and its early symptoms are hidden, therefore, it is of significant research value to develop early-stage detection methods of liver cancer for pathological screening. In this paper, a biometric detection method for living human hepatocytes based on terahertz time-domain spectroscopy was proposed. The difference in terahertz response between normal and cancer cells was analyzed, including five characteristic parameters in the response, namely refractive index, absorption coefficient, dielectric constant, dielectric loss and dielectric loss tangent. Based on class separability and variable correlation, absorption coefficient and dielectric loss were selected to better characterize cellular properties. Maximum information coefficient and principal component analysis were employed for feature extraction, and a cell classification model of support vector machine was constructed. The results showed that the algorithm based on parameter feature fusion can achieve an accuracy of 91.6% for human hepatoma cell lines and one normal cell line. This work provides a promising solution for the qualitative evaluation of living cells in liquid environment.

[Terahertz time-domain spectroscopy of alpha endosulfan persistent organic pollutant].

Frequency-dependent absorption coefficient spectrum and refractive index spectrum of alpha endosulfan, a kind of persistent organic pollutants, are presented in the terahertz frequency region by terahertz (THz) time-domain spectroscopy (TDS). The spectral features in the THz region have a number of unique characteristic absorption peaks. The result demonstrates that THz-TDS is a promising method to identify materials. Then we adopted density functional calculation method to analyze theoretic absorption coefficients of single alpha endosulfan molecule within 0.2-2 THz. The results show that absorption peaks at 1.68, 1.91 THz in theoretical calculation correspond to 1.7, 1.88 THz in the experiment. Finally, vibrational modes and approximate assignments were discussed, showing that these matched peaks are caused by intramolecular vibrational modes of alpha endosulfan. Others might be related to intermolecular vibrational modes or combined vibrational modes.

[Spectral analysis of nitrofurantoin in the terahertz frequency range].

The present article measured the absorption coefficient spectra and refractive index spectra of nitrofurantoin original drug, which is one kind of nitrofuran drugs, in the terahertz frequency range from 0.2 to 1.8 THz using terahertz time-domain spectroscopy. The results showed that there exist a number of characteristic absorption peaks of nitrofurantoin with different intensity in the range and the absorption coefficient spectra can be used to identify nitrofurantoin. The article also simulated absorption coefficient spectra of nitrofurantoin molecule within 0.2 - 1.8 THz using density functional theory by Gaussian software, and vibrational modes of some peaks in the experimental absorption coefficient spectra were analyzed and identified. The results show that the experimental absorption peaks at 1.25 and 1.60 THz correspond with the theoretical peaks at 1.30 and 1.67 THz, and these experimental peaks were caused by intramolecular vibrational modes of nitrofurantoin.

[Terahertz spectroscopic investigation of elaidic acid].

The optical properties of elaidic acid in the THz band were investigated by using THz time-domain spectroscopy (THZ-TDS). The absorption and refractive index spectra in the frequency range from 0.5 to 2.5 THz were obtained at room temperature under nitrogen atmosphere, and the results show that elaidic acid had some characteristic absorption peaks, and the average refractive index of the sample was 1.43. The structure and vibrational frequencies of elaidic acid molecule in the THz range were simulated by the B3LYP algorithm of density functional theory, and the THz characteristic absorption peaks of elaidic acid molecule were identified by using Gaussian View software. The results show that the experimental and theoretical results agree in general, and the observed absorption peaks in the THz range were mainly caused by the intramolecular and intermolecular vibrations jointly. At the same time, the fingerprint spectra of elaidic acid in the THz band verify that the time-domain terahertz spectroscopy can be used to detect oleic acid, and this study provides a new experimental method for the detection of trans fatty acids in food.

[Terahertz time-domain spectroscopy of Clenbuterol hydrochloride].

The terahertz spectra of Clenbuterol hydrochloride in the range of 0.2 to 2.6 THz were obtained by THz time-domain spectroscopy, the absorption and refraction spectra of Clenbuterol hydrochloride was got meanwhile. The structure and vibrational frequencies of Clenbuterol molecule, Clenbuterol hydrochloride molecule and Clenbuterol hydrochloride crystal in the THz range were simulated. Based on the difference between experimental and theoretical results, the origin of the vibrational frequencies was analyzed. This study demonstrated the feasibility of time-domain terahertz spectroscopy for the identification of Clenbuterol hydrochloride and provides a new way for the detection of Clenbuterol hydrochloride.

[Terahertz time-domain spectroscopy of ractopamine hydrochloride].

The terahertz spectra of Ractopamine hydrochloride in the range of 0.2 to 2.2 THz was obtained by THz time-domain spectroscopy, and the absorption and refraction spectra of Ractopamine hydrochloride was got meanwhile. The structure and vibrational frequencies of Ractopamine molecule in the THz range were simulated by density functional theory. The difference between experimental and theoretical results was analyzed. And assisted by Gaussian View 3.09, the origin of the vibrational frequencies was recognized. The results show that besides the intramolecular vibrations, THz absorption of Ractopamine hydrochloride originated from the intermolecular hydrogen bond network and Van der Waals force between molecules. This study demonstrated the feasibility of time-domain terahertz spectroscopy for the identification of Ractopamine hydrochloride and provided a new way for the detection of Ractopamine hydrochloride.

Characterization and discrimination of human colorectal cancer cells using terahertz spectroscopy.

Terahertz technology has been widely used in biomedical research. Herein, terahertz time-domain attenuated total reflection (THz TD-ATR) spectroscopy was employed to characterize and discriminate human cancer cell lines (DLD-1 and HT-29). Terahertz responses of the cell lines were measured and Savitzky-Golay algorithm was applied to smooth the spectra of refractive index, absorption coefficient and dielectric loss tangent in terahertz regime. Principal component analysis (PCA) was then adopted for feature extraction and cell characterization. Based on the processed data, cancer cell lines were discriminated by applying random forests (RF) method to analyze three characteristic parameters separately and the results from them were compared. Results indicate that absorption coefficient was the most sensitive parameter for cancer cell discrimination. Our study suggests great potential for human cancer cell recognition and provides experimental basis for liquid biopsy.

Qualitative and quantitative detection of liver injury with terahertz time-domain spectroscopy.

Terahertz technology has been widely used as a nondestructive and effective detection method. Herein, terahertz time-domain spectroscopy was used to detect drug-induced liver injury in mice. Firstly, the boxplots were used to detect abnormal data. Then the maximal information coefficient method was used to search for the features strongly correlated with the degree of injury. After that, the liver injury model was built using the random forests method in machine learning. The results show that this method can effectively identify the degree of liver injury and thus provide an auxiliary diagnostic method for detecting minor liver injury.

Study on glycoprotein terahertz time-domain spectroscopy based on composite multiscale entropy feature extraction method.

Tumor genesis is accompanied by glycosylation of related proteins. Glycoprotein is usually regarded as a tumor marker since glycoproteins are consumed remarkably more by the cancer cells than the normal ones. In this paper, the terahertz time-domain attenuated total reflection (ATR) technique is applied to inspect the glycoprotein solution from a concentration gradient of 0.2 mg/ml to 50 mg/ml. A significant nonlinear relationship between the absorption coefficient and the concentrations has been discovered. The influence of the dynamical hydration shell around glycoprotein molecules on the absorption coefficient is discussed and the phenomenon is explained by the concepts of THz excess and THz defect. In order to identify glycoproteins, features are obtained by composite multiscale entropy (CMSE) method and clustered by the K-means algorithm. The results indicate that features extracted by the CMSE method are better than the Principal Component Analysis (PCA) method in both specificity and sensitivity of recognition. Meanwhile, the absorption coefficient and dielectric loss angle tangent are more suitable for qualitative identification. Research shows that the CMSE method has important directive significance for analyzing glycoprotein terahertz spectroscopy. And it has the potential for glycoprotein related tumor markers identification using terahertz technology in medical applications.

[Terahertz time-domain spectroscopy of L-ascorbic acid].

The present paper reports the theoretical and experimental vibrational frequencies of L-ascorbic acid in the frequency range from 0.2 to 2.4 THz respectively. The vibrational frequencies of L-ascorbic acid in the THz range were simulated by Becke-3-Lee-Yang-Parr (B3LYP) algorithm of density functional theory. Simultaneously, the absorption and refractive index spectra of L-ascorbic acid were obtained using terahertz time-domain spectroscopy (THz-TDS). Based on the results from density functional calculations, the observed vibrational peaks were assigned with the aid of Gaussian View 3.07. Finally, the tera-hertz absorption spectra of tablets of vitamin C were compared with those of L-ascorbic acid. It was found that L-ascorbic acid had some distinct fingerprint spectra in the THz region and the theoretical results were in good agreement with the experimental results. The observed vibrational peaks were induced jointly by the intramolecular and intermolecular vibrations. The absorption peaks of L-ascorbic acid could be observed in the absorption spectra of the tablets of vitamin C. Our research is helpful to understanding the role of L-ascorbic acid in biological reactions. Still, the research results show the potential of the application of THz-TDS in the quantification of L-ascorbic acid.

Inspecting human colon adenocarcinoma cell lines by using terahertz time-domain reflection spectroscopy.

Techniques to inspect and analyze human colorectal cancer cell lines by using terahertz time-domain attenuated total reflection spectroscopy (THz TD-ATR) were investigated. The characteristics of THz absorption spectra of two colorectal cancer cell lines DLD-1 and HT-29 in aqueous solutions with different concentrations were studied. Different spectral features were observed compared to normal cell line. Identification results based on different parameters including absorption coefficient, refractive index, real and imaginary parts of complex permittivity, dielectric loss tangent were discussed. This research may be promising for quick and instant inspection of liquid samples by using THz time-domain spectroscopy in medical applications.

Terahertz spectral unmixing based method for identifying gastric cancer.

At present, many researchers are exploring biological tissue inspection using terahertz time-domain spectroscopy (THz-TDS) techniques. In this study, based on a modified hard modeling factor analysis method, terahertz spectral unmixing was applied to investigate the relationships between the absorption spectra in THz-TDS and certain biomarkers of gastric cancer in order to systematically identify gastric cancer. A probability distribution and box plot were used to extract the distinctive peaks that indicate carcinogenesis, and the corresponding weight distributions were used to discriminate the tissue types. The results of this work indicate that terahertz techniques have the potential to detect different levels of cancer, including benign tumors and polyps.