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This paper introduced an analytical solution and improved one-factor Gaussian copula models to the pricing of tranches of a Collateralized debt obligations (CDO) portfolio. Prices of CDO tranches are calculated and compared using the analytical model and different one-factor Gaussian copula models including a two-category heterogeneous model and a completely heterogeneous model that uses individual rate parameter and correlation coefficient for each reference entity in a CDO portfolio. When correlation among reference entities is low, the price calculated from the analytical model matches very well with the one-factor Gaussian copula models. However, as the correlation among reference entities increases, prices calculated using both the analytical solution and the homogeneous or two-category one-factor Gaussian copula models significantly deviate from the completely heterogeneous one-factor Gaussian copula model. This result verifies our belief that uniform parameters cannot completely capture all the heterogeneities in a CDO portfolio. Completely heterogeneous one-factor Gaussian copula model using individual rate parameters and correlation coefficients for each reference entities provides more reliable and accurate prices for structured securities.

Native fluorescence spectra are acquired from fresh normal and cancerous human prostate tissues. The fluorescence data are analyzed using an unsupervised machine learning algorithm such as non-negative matrix factorization. The nonnegative spectral components are retrieved and attributed to the native fluorophores such as collagen, reduced nicotinamide adenine dinucleotide (NADH), and flavin adenine dinucleotide (FAD) in tissue. The retrieved scores of the components are used to estimate the relative concentrations of the native fluorophores such as NADH and FAD and the redox ratio. A supervised machine learning algorithm such as support vector machine (SVM) is used to classify normal and cancerous tissue samples based on either the relative concentrations of NADH and FAD or the redox ratio alone. Various statistical measures such as sensitivity, specificity, and accuracy, along with the area under receiver operating characteristic (ROC) curve are used to show the classification performance. A cross validation method such as leave-one-out is used to further evaluate the predictive performance of the SVM classifier to avoid bias due to overfitting, and the accuracy was found to be 93.3%.

Distinguishing chromophobe renal cell carcinoma (chRCC) from oncocytoma on hematoxylin and eosin images may be difficult and require time-consuming ancillary procedures. Multiphoton microscopy (MPM), an optical imaging modality, was used to rapidly generate sub-cellular histological resolution images from formalin-fixed unstained tissue sections from chRCC and oncocytoma. Tissues were excited using 780nm wavelength and emission signals (including second harmonic generation and autofluorescence) were collected in different channels between 390 nm and 650 nm. Granular structure in the cell cytoplasm was observed in both chRCC and oncocytoma. Quantitative morphometric analysis was conducted to distinguish chRCC and oncocytoma. To perform the analysis, cytoplasm and granules in tumor cells were segmented from the images. Their area and fluorescence intensity were found in different channels. Multiple features were measured to quantify the morphological and fluorescence properties. Linear support vector machine (SVM) was used for classification. Re-substitution validation, cross validation and receiver operating characteristic (ROC) curve were implemented to evaluate the efficacy of the SVM classifier. A wrapper feature algorithm was used to select the optimal features which provided the best predictive performance in separating the two tissue types (classes). Statistical measures such as sensitivity, specificity, accuracy and area under curve (AUC) of ROC were calculated to evaluate the efficacy of the classification. Over 80% accuracy was achieved as the predictive performance. This method, if validated on a larger and more diverse sample set, may serve as an automated rapid diagnostic tool to differentiate between chRCC and oncocytoma. An advantage of such automated methods are that they are free from investigator bias and variability. Copyright © 2016 SPIE.

Time reversal optical tomography (TROT), a recently introduced diffuse optical imaging approach, is used to detect, locate, and obtain cross-section images of tumors inside a "model human breast." The model cancerous breast is assembled as a semi-cylindrical slab of uniform thickness using ex vivo human breast tissues with two pieces of tumors embedded in it. The experimental arrangement used a 750-nm light beam from a Ti:sapphire laser to illuminate an end face (source plane) of the sample in a multi-source probing scheme. A multi-detector signal acquisition scheme measured transmitted light intensity distribution on the other end face (detector plane). The perturbations in light intensity distribution in the detector plane were analyzed using TROT to obtain locations of the tumor pieces in three dimensions and estimate their cross sections. The estimated locations and dimensions of targets are in good agreement with the results of a corroborating magnetic resonance imaging experiment. © 2015 Elsevier Ltd. All rights reserved.

Native fluorescence spectra are acquired from fresh normal and cancerous human prostate tissues. The fluorescence data are analyzed using a multivariate analysis algorithm such as non-negative matrix factorization. The nonnegative spectral components are retrieved and attributed to the native fluorophores such as collagen, reduced nicotinamide adenine dinucleotide (NADH), and flavin adenine dinucleotide (FAD) in tissue. The retrieved weights of the components, e.g. NADH and FAD are used to estimate the relative concentrations of the native fluorophores and the redox ratio. A machine learning algorithm such as support vector machine (SVM) is used for classification to distinguish normal and cancerous tissue samples based on either the relative concentrations of NADH and FAD or the redox ratio alone. The classification performance is shown based on statistical measures such as sensitivity, specificity, and accuracy, along with the area under receiver operating characteristic (ROC) curve. A cross validation method such as leave-one-out is used to evaluate the predictive performance of the SVM classifier to avoid bias due to overfitting.

Native fluorescence spectra of retinoic acid (RA)-treated and untreated human breast cancer cells were measured using selective wavelengths of 300 nm and 340 nm for excitation. The spectral data of the two types of cells were analyzed using machine learning algorithms for linear unmixing and classification which yielded high accuracy. The results show that the concentrations of the native fluorophores such as tryptophan, NADH and flavins in the human malignant breast cells change when they are treated with RA. The study shows the dual-wavelength fluorescence spectroscopy aided by machine learning has potential clinical applications in drug development and chemotherapeutic studies.

In this study, nanoparticles of pure zinc oxide (ZnO) and ZnO doped with iron of various doping concentrations (Zn_{1- x}Fe_xO) are analyzed using fluorescence spectroscopy. Excitation and emission spectra using various operating wavelengths were collected. Individual spectra and excitation emission matrix were analyzed. Various peaks including strong ultraviolet (UV) emission peaks and strong blue emission peaks that are corresponding to the near-band-edge emission (NBE) and defect emission (DE) peaks were studied based on the peak intensities, peak wavelengths, and NBE peak to defect peak ratios. The Zn_1-xFe_xO materials were also analyzed using X-ray diffraction and optical absorption spectroscopy. The variation in the band gap energy and in the NBE emission energy with dopant concentration was analyzed. A red-shift was observed with the NBE emission peak. The NBE to DE ratio initially increases from pure ZnO to Zn_0.97Fe_0.03O and then decreases as the dopant concentration increases.

Food spoilage is mainly caused by microorganisms, such as bacteria. In this study, we measure the autofluorescence in meat samples longitudinally over a week in an attempt to develop a method to rapidly detect meat spoilage using fluorescence spectroscopy. Meat food is a biological tissue, which contains intrinsic fluorophores, such as tryptophan, collagen, nicotinamide adenine dinucleotide (NADH) and flavin adenine dinucleotide (FAD) etc. As meat spoils, it undergoes various morphological and chemical changes. The concentrations of the native fluorophores present in a sample may change. In particular, the changes in NADH and FAD are associated with microbial metabolism, which is the most important process of the bacteria in food spoilage. Such changes may be revealed by fluorescence spectroscopy and used to indicate the status of meat spoilage. Therefore, such native fluorophores may be unique, reliable and nonsubjective indicators for detection of spoiled meat. The results of the study show that the relative concentrations of all above fluorophores change as the meat samples kept in room temperature (~19° C) spoil. The changes become more rapidly after about two days. For the meat samples kept in a freezer (~-12° C), the changes are much less or even unnoticeable over a-week-long storage.

Food spoilage is mainly caused by microorganisms, such as bacteria. In this study, we measure the autofluorescence in meat samples longitudinally over a week in an attempt to develop a method to rapidly detect meat spoilage using fluorescence spectroscopy. Meat food is a biological tissue, which contains intrinsic fluorophores, such as tryptophan, collagen, nicotinamide adenine dinucleotide (NADH) and flavin adenine dinucleotide (FAD) etc. As meat spoils, it undergoes various morphological and chemical changes. The concentrations of the native fluorophores present in a sample may change. In particular, the changes in NADH and FAD are associated with microbial metabolism, which is the most important process of the bacteria in food spoilage. Such changes may be revealed by fluorescence spectroscopy and used to indicate the status of meat spoilage. Therefore, such native fluorophores may be unique, reliable and non-subjective indicators for detection of spoiled meat. The results of the study show that the relative concentrations of all above fluorophores change as the meat samples kept in room temperature (~19°C) spoil. The changes become more rapidly after about two days. For the meat samples kept in a freezer (~ -12°C), the changes are much less or even unnoticeable over a-week-long storage.

Convolutional neural networks (CNN) are a class of machine learning model that are especially well suited for imagebased tasks. In this study, we design and train a CNN on tissue samples imaged using Multi-Photon Microscopy (MPM) and show that the model can distinguish between chromophobe renal cell carcinoma (chRCC) and oncocytoma. We demonstrate the method to train a model using simple max-pooling vote fusion, and use the model to highlight regions of the input that cause a positive classification. The model can be tuned for higher sensitivity at the cost of specificity with a constant threshold and little impact to accuracy overall. Several numerical experiments were run to measure the model’s accuracy on both image and patient level analysis. Our models were designed with a dropout parameter that biases the model towards higher sensitivity or specificity. Our best performance model, as measured by area under the receiver operating characteristic curve (AUC of ROC, or AUROC) on patient level classification, is measured with a 94% AUROC and 88% accuracy, along with 100% sensitivity and 75% specificity.

Context.-Distinguishing chromophobe renal cell carcinoma (chRCC), especially in the presence of eosinophilic cytoplasm, from oncocytoma on hematoxylin-eosin can be difficult and often requires time-consuming ancillary procedures that ultimately may not be informative. Objective.-To explore the potential of multiphoton microscopy (MPM) as an alternative and rapid diagnostic tool in differentiating oncocytoma from chRCC at subcellular resolution without tissue processing. Design.-Unstained, deparaffinized tissue sections from 27 tumors (oncocytoma [n = 12], chRCC [n = 12], eosinophilic variant of chRCC [n = 1], and atypical oncocytic renal neoplasm [n = 2]) were imaged with MPM. Morphologic evaluation and automated quantitative morphometric analysis were conducted to distinguish between chRCC and oncocytoma. Results.-The typical cases of oncocytomas (12 of 12) and chRCC (12 of 12) could be readily differentiated on MPM based on the morphologic features similar to hematoxylin-eosin. The most striking MPM signature of both of the tumors was the presence of autofluorescent intracytoplasmic granules, which are not seen on hematoxylin-eosin-stained slides. Although we saw these granules in both types of tumors, they appeared distinct, based on their size, shape, cytoplasmic distribution, and autofluorescence wavelengths, and were valuable in arriving at a definitive diagnosis. For oncocytomas and chRCC, high diagnostic accuracies of 100% and 83.3% were achieved on blinded MPM and morphometric analysis, respectively. Conclusions.-To the best of our knowledge, this is the first demonstration of MPM to distinguish chRCC from oncocytoma in fixed tissues. Our study was limited by small sample size and only a few variants of oncocytic tumors. Prospective studies are warranted to assess the utility of MPM as a diagnostic aid in oncocytic renal tumors. © Copyright 2018 College of American Pathologists.

Worldwide breast cancer incidence has increased by more than twenty percent in the past decade. It is also known that in that time, mortality due to the affliction has increased by fourteen percent. Using optical-based diagnostic techniques, such as Raman spectroscopy, has been explored in order to increase diagnostic accuracy in a more objective way along with significantly decreasing diagnostic wait-times. In this study, Raman spectroscopy with 532-nm excitation was used in order to incite resonance effects to enhance Stokes Raman scattering from unique biomolecular vibrational modes. Seventy-two Raman spectra (41 cancerous, 31 normal) were collected from nine breast tissue samples by performing a ten-spectra average using a 500-ms acquisition time at each acquisition location. The raw spectral data was subsequently prepared for analysis with background correction and normalization. The spectral data in the Raman Shift range of 750- 2000 cm-1 was used for analysis since the detector has highest sensitivity around in this range. The matrix decomposition technique nonnegative matrix factorization (NMF) was then performed on this processed data. The resulting leave-oneout cross-validation using two selective feature components resulted in sensitivity, specificity and accuracy of 92.6%, 100% and 96.0% respectively. The performance of NMF was also compared to that using principal component analysis (PCA), and NMF was shown be to be superior to PCA in this study. This study shows that coupling the resonance Raman spectroscopy technique with subsequent NMF decomposition method shows potential for high characterization accuracy in breast cancer detection.

Metastasis is the leading cause of mortalities in cancer patients due to the spreading of cancer cells to various organs. Detecting cancer and identifying its metastatic potential at the early stage is important. This may be achieved based on the quantification of the key biomolecular components within tissues and cells using recent optical spectroscopic techniques. The aim of this study was to develop a noninvasive label-free optical biopsy technique to retrieve the characteristic molecular information for detecting different metastatic potentials of prostate cancer cells. Herein we report using native fluorescence (NFL) spectroscopy along with machine learning (ML) to differentiate prostate cancer cells with different metastatic abilities. The ML algorithms including principal component analysis (PCA) and nonnegative matrix factorization (NMF) were used for dimension reduction and feature detection. The characteristic component spectra were used to identify the key biomolecules that are correlated with metastatic potentials. The relative concentrations of the molecular spectral components were retrieved and used to classify the cancer cells with different metastatic potentials. A multi-class classification was performed using support vector machines (SVMs). The NFL spectral data were collected from three prostate cancer cell lines with different levels of metastatic potentials. The key biomolecules in the prostate cancer cells were identified to be tryptophan, reduced nicotinamide adenine dinucleotide (NADH) and hypothetically lactate as well. The cancer cells with different metastatic potentials were classified with high accuracy using the relative concentrations of the key molecular components. The results suggest that the changes in the relative concentrations of these key fluorophores retrieved from NFL spectra may present potential criteria for detecting prostate cancer cells of different metastatic abilities.

Early detection of prostate cancer is critical for the success of cancer therapy. It is believed that the biochemical changes that cause the optical spectra changes would appear earlier than the histological aberration. The aim of this ex vivo study was to evaluate the ability of Stokes Shift Spectra (S3) to identify human prostate cancerous tissues from the normal. Fifteen (15) pairs of with pathologically confirmed human prostate cancerous and normal tissues underwent Stokes Shift Spectra measurements with selective wavelength interval of 40 nm. The spectra were then analyzed using machine learning (ML) algorithms to classify the two types of tissues. The ML algorithms including principal component analysis (PCA) and nonnegative matrix factorization (NMF) were used for dimension reduction and feature detection. The characteristic component spectra were used to identify the key fluorophores related to carcinogenesis. The results show that these key fluorophores within tissue, e.g., tryptophan, collagen, and NADH, have different relative concentrations between cancerous and normal tissues. A multi-class classification was performed using support vector machines (SVMs). A leave-one-out cross validation was used to evaluate the performance of the classification with the gold standard histopathological results as the ground truth. The results with high sensitivity and specificity indicate that the S3 method is effective for detecting changes of fluorophore composition in human prostate tissues due to the development of cancer. © 2021 SPIE.

This chapter first reviews the research and applications of nonresonance and resonance Raman spectroscopy for analysis of human brain normal and abnormal tissues. Next, special emphasis is made on our recent achievements of visible resonance Raman (VRR) technique in primary human brain tumor disease investigation and diagnosis. Visible resonance Raman (VRR) spectroscopy technique uses excitation of visible light (532 nm) to evaluate the resonant and nonresonant molecular vibrational modes in biological tissues. The VRR signal intensities are enhanced by two to three orders of magnitude for faster use in medical applications in quasi real time. VRR opens up a new stainless “molecular optics based histopathology” diagnosis approach. © 2019 Elsevier Ltd. All rights reserved.

Multiphoton microscopy images of chromophobe renal cell carcinoma and renal oncocytoma were classified using a convolutional neural network inspired by techniques in recent architectures and yielded over 70% accuracy.

A new criterion was developed to characterize brain tissue using resonance Raman spectroscopy, by which, negative margins of cancer can be differentiated from normal tissues. This method may help a surgeon better decide surgical margins. © OSA 2017.

We report on the use of label-free, native fluorescence (NFL) spectroscopy and machine learning (ML) algorithms to study the correlation of relative tryptophan levels with prostate cancer aggressiveness. Three extensively studied prostate cancer cell lines were used; PC3, an aggressive, androgen-resistant line, with a high tendency to metastasize in vivo, DU-145, a less aggressive cancer cell line, also androgen-resistant, and LNCaP, an androgen sensitive line, which has a low tendency to metastasize. Using an excitation of 300nm, differences in the NFL spectral profiles from these cell lines were found to correlate with changes in the relative concentrations of tryptophan and reduced nicotinamide adenine dinucleotide (NADH). The use of ML may present a powerful tool for the assessment of the likelihood of a cancer to metastasize. This technique could aid in the decision whether to use highly aggressive adjuvant chemotherapy or radiation therapy after surgical resection of a prostate cancer.