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Raman spectroscopy is a molecular vibrational spectroscopic technique that is capable of optically probing the biomolecular changes associated with diseased transformation. The purpose of this study was to explore near-infrared (NIR) Raman spectroscopy for identifying dysplasia from normal gastric mucosa tissue. A rapid-acquisition dispersive-type NIR Raman system was utilised for tissue Raman spectroscopic measurements at 785 nm laser excitation. A total of 76 gastric tissue samples obtained from 44 patients who underwent endoscopy investigation or gastrectomy operation were used in this study. The histopathological examinations showed that 55 tissue specimens were normal and 21 were dysplasia. Both the empirical approach and multivariate statistical techniques, including principal components analysis (PCA), and linear discriminant analysis (LDA), together with the leave-one-sample-out crossvalidation method, were employed to develop effective diagnostic algorithms for classification of Raman spectra between normal and dysplastic gastric tissues. High-quality Raman spectra in the range of 800 -1800 cm À1 can be acquired from gastric tissue within 5 s. There are specific spectral differences in Raman spectra between normal and dysplasia tissue, particularly in the spectral ranges of 1200 -1500 cm À1 and 1600 -1800 cm À1 , which contained signals related to amide III and amide I of proteins, CH 3 CH 2 twisting of proteins/nucleic acids, and the C ¼ C stretching mode of phospholipids, respectively. The empirical diagnostic algorithm based on the ratio of the Raman peak intensity at 875 cm À1 to the peak intensity at 1450 cm À1 gave the diagnostic sensitivity of 85.7% and specificity of 80.0%, whereas the diagnostic algorithms based on PCA-LDA yielded the diagnostic sensitivity of 95.2% and specificity 90.9% for separating dysplasia from normal gastric tissue. Receiver operating characteristic (ROC) curves further confirmed that the most effective diagnostic algorithm can be derived from the PCA-LDA technique. Therefore, NIR Raman spectroscopy in conjunction with multivariate statistical technique has potential for rapid diagnosis of dysplasia in the stomach based on the optical evaluation of spectral features of biomolecules.

The swimbladder is a hydrostatic organ in fish postulated as a homolog of the tetrapod lung. While lung development has been well studied, the molecular mechanism of swimbladder development is essentially uncharacterized. In the present study, swimbladder development in zebrafish was analyzed by using several molecular markers: hb9 (epithelium), fgf10a and acta2 (mesenchyme), and anxa5 (mesothelium), as well as in vivo through enhancer trap transgenic lines Et(krt4:EGFP)(sq33-2) and Et(krt4:EGFP)(sqet3) that showed strong EGFP expression in the swimbladder epithelium and outer mesothelium respectively. We defined three phases of swimbladder development: epithelial budding between 36 and 48 hpf, growth with the formation of two additional mesodermal layers up to 4.5 dpf, and inflation of posterior and anterior chambers at 4.5 and 21 dpf respectively. Similar to those in early lung development, conserved expression of Hedgehog (Hh) genes, shha and ihha, in the epithelia, and Hh receptor genes, ptc1 and ptc2, as well as fgf10a in mesenchyme was observed. By analyzing several mutants affecting Hh signaling and Ihha morphants, we demonstrated an essential role of Hh signaling in swimbladder development. Furthermore, time-specific Hh inhibition by cyclopamine revealed different requirements of Hh signaling in the formation and organization of all three tissue layers of swimbladder.

Short-term traffic flow prediction has long been regarded as a critical concern for intelligent transportation systems. On the basis of many existing prediction models, each having good performance only in a particular period, an improved approach is to combine these single predictors together for prediction in a span of periods. In this paper, a neural network model is introduced that combines the prediction from single neural network predictors according to an adaptive and heuristic credit assignment algorithm based on the theory of conditional probability and Bayes' rule. Two single predictors, i.e., the back propagation and the radial basis function neural networks are designed and combined linearly into a Bayesian combined neural network model. The credit value for each predictor in the combined model is calculated according to the proposed credit assignment algorithm and largely depends on the accumulative prediction performance of these predictors during the previous prediction intervals. For experimental test, two data sets comprising traffic flow rates in 15-min time intervals have been collected from Singapore's Ayer Rajah Expressway. One data set is used to train the two single neural networks and the other to test and compare the performances between the combined and singular models. Three indices, i.e., the mean absolute percentage error, the variance of absolute percentage error, and the probability of percentage error, are employed to compare the forecasting performance. It is found that most of the time, the combined model outperforms the singular predictors. More importantly, for a given time period, it is the role of this newly proposed model to track the predictors' performance online, so as to always select and combine the best-performing predictors for prediction.

Raman spectroscopy is a vibrational spectroscopic technique capable of optically probing the biomolecular changes associated with neoplastic transformation. The purpose of this study was to apply near-infrared (NIR) Raman spectroscopy in the high wavenumber (HW) region (2800-3700 cm(-1)) for in vivo detection of cervical dysplasia. A rapid-acquisition NIR Raman spectroscopy system associated with a ball-lens fiber-optic Raman probe was developed for in vivo spectroscopic measurements at 785 nm excitation. A total of 92 in vivo HW Raman spectra (46 normal, 46 dysplasia) were acquired from 46 patients with Pap smear abnormalities of the cervix. Significant difference in Raman intensities of prominent Raman bands at 2850 and 2885 cm(-1) (CH(2) stretching of lipids), 2940 cm(-1) (CH(3) stretching of proteins), and the broad Raman band of water (peaking at 3400 cm(-1) in the 3100-3700 cm(-1) range) were observed in normal and dysplasia cervical tissue. The diagnostic algorithms based on principal components analysis and linear discriminant analysis together with the leave-one-patient-out cross-validation method on in vivo HW Raman spectra yielded a diagnostic sensitivity of 93.5% and specificity of 97.8% for dysplasia tissue identification. This study demonstrates for the first time that HW Raman spectroscopy has the potential for the noninvasive, in vivo diagnosis and detection of precancer of the cervix.

We report an integrated Raman spectroscopy and trimodal (white-light reflectance, autofluorescence, and narrow-band) imaging techniques for real-time in vivo tissue Raman measurements at endoscopy. A special 1.8 mm endoscopic Raman probe with filtering modules is developed, permitting effective elimination of interference of fluorescence background and silica Raman in fibers while maximizing tissue Raman collections. We demonstrate that high-quality in vivo Raman spectra of upper gastrointestinal tract can be acquired within 1 s or subseconds under the guidance of wide-field endoscopic imaging modalities, greatly facilitating the adoption of Raman spectroscopy into clinical research and practice during routine endoscopic inspections.

Abstract. The ability of combining near-infrared (NIR) Raman spectroscopy with support vector machines (SVM) for improving multi-class classification between different histopathological groups in tissues was evaluated in this study. A total of 105 colonic tissue specimens from 59 patients including 41 normal, 18 hyperplastic polyps and 46 adenocarcinomas were used for this purpose. A rapid-acquisition dispersive-type NIR Raman system was utilized for tissue Raman spectroscopic measurements at 785-nm laser excitation. A total of 817 tissue Raman spectra were acquired and subjected to principal components analysis (PCA) for SVM-based multi-class classification, in which 324 Raman spectra were from normal, 184 from polyps and 309 from adenocarcinomatous colonic tissue. Two types of SVM (i.e., C-SVM and ν-SVM) with three different kernel functions (linear, polynomial and Gaussian radial basis function (RBF) in combination with PCA were used to develop effective diagnostic algorithms for classification of Raman spectra of different colonic tissues. The performance of various SVMbased algorithms was evaluated and compared using a leave-one-out, cross-validation method. The results showed that in the C-SVM classification, the maximum overall diagnostic accuracy of 99.3, 99.4 and 99.9% can be achieved using the linear, polynomial and RBF kernels, respectively; while in the ν-SVM classification, the maximum overall diagnostic accuracy of 98.4, 98.5 and 99.6% can be obtained using the linear, polynomial and RBF kernels, respectively. All the polyps can be identified from normal and adenocarcinomatous tissue using the C-SVM algorithms. The RBF C-SVM algorithm was proven to be the best classifier for providing the highest diagnostic accuracy (99.9%) for multi-class classification. This study demonstrates that NIR Raman spectroscopy in combination with a powerful SVM technique has great potential for providing an effective and accurate diagnostic schema for cancer diagnosis in the colon.

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