Introducing an analytical solution and an improved one-factor gaussian copula model for the pricing of heterogeneous CDOs - Gao, Xin; Wu, Binlin; Schäfer, Tobias (International Journal of Financial Engineering, 2017)
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.
0 citations (Crossref) [2023-10-31] Citation Key Alias: lens.org/183-625-470-365-466, pop00170
Keywords: dept.phy

Optical biopsy using fluorescence spectroscopy for prostate cancer diagnosis - Wu, Binlin; Gao, Xin; Smith, Jason; Bailin, Jacob (Therapeutics and Diagnostics in Urology: Lasers, Robotics, Minimally Invasive, and Advanced Biomedical Devices, 2017)
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.
1 citations (Crossref) [2023-10-31] Citation Key Alias: lens.org/116-092-737-895-249, pop00138 tex.type: [object Object]
Keywords: Artificial intelligence; Biopsy; Cross validation; Diseases; Factorization; Fluorescence; Fluorescence spectroscopy; Fluorophores; Learning algorithms; Learning systems; Matrix algebra; Multivariant analysis; Nonnegative matrix factorization; Nucleotides; Optical biopsies; Prostate cancers; Redox ratio; Robotics; Support vector machines; Tissue; Urology; cross validation; dept.phy; fluorescence spectroscopy; machine learning; nonnegative matrix factorization; optical biopsy; prostate cancer; redox ratio; support vector machine

Rapid measurement of meat spoilage using fluorescence spectroscopy - Wu, Binlin; Dahlberg, Kevin; Gao, Xin; Smith, Jason; Bailin, Jacob (Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XV, 2017)
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.
1 citations (Crossref) [2023-10-31] Citation Key Alias: lens.org/026-929-135-501-547, pop00136 tex.type: [object Object]
Keywords: Amino acids; Bacteria; Biological tissues; Biomolecules; Collagen; Flavin adenine dinucleotide; Fluorescence; Fluorescence spectroscopy; Fluorophores; Food microbiology; Histology; Meat food; Meats; Metabolism; Microbial metabolism; Molecular biology; NADH; Nicotinamide adenine dinucleotides; Nucleotides; Physiology; Rapid measurement; Relative concentration; Spectrum analysis; Spoilage; Tissue; Tryptophan; dept.phy

Quantitative characterization for identification of brain cancer surgical margin using resonance Raman spectroscopy - Zhou, Yan; Liu, Chenghui; Wu, Binlin; Yu, Xinguang; Cheng, Gangge; Zhang, Chunyuan; Alfano, R R (Frontiers in Optics, 2017)
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.
4 citations (Crossref) [2023-10-31] Citation Key Alias: lens.org/043-005-059-782-813 tex.type: [object Object]
Keywords: Brain cancer; Brain tissue; Diseases; Normal tissue; Quantitative characterization; Raman spectroscopy; Resonance Raman spectroscopy; Surgery; Tissue; dept.phy

A component‐by‐component characterisation of high‐risk atherosclerotic plaques by multiphoton microscopic imaging - Jain, Manu; Wu, Binlin; Pisapia, David J; Salvatore, Steven P; Mukherjee, Sushmita; Narula, Navneet; Mukherjee, Sushmita; Narula, Navneet (Journal of Microscopy, 2017)
Aims: Atherosclerotic plaques vulnerable to rupture are almost always inflamed, and carry a large lipid core covered by a thin fibrous cap. The other components may include neovascularisation, intraplaque haemorrhage and spotty calcification. In contrast, stable plaques are characterised by a predominance of smooth muscle cells and collagen, and lipid core is usually deep seated or absent. This study is a proof of principle experiment to evaluate the feasibility of multiphoton microscopy (MPM) to identify aforementioned plaque components. Methods and Results: MPM is a nonlinear optical technique that allows imaging based on intrinsic tissue signals including autofluorescence and higher-order scattering. In our study, MPM imaging was performed on morphologically diverse aortic and coronary artery plaques obtained during autopsy. Various histologically verified plaque components including macrophages, cholesterol crystals, haemorrhage, collagen and calcification were recognised by MPM. Conclusions: Recognition of the distinct signatures of various plaque components suggests that MPM has the potential to offer next-generation characterisation of atherosclerotic plaques. The higher lateral resolution (comparable to histology) images generated by MPM for identifying plaque components might complement larger field of view and greater imaging depth currently available with optical coherence tomography imaging. As the next step MPM would need to be evaluated for intact vessel imaging ex vivo and in vivo. © 2017 The Authors Journal of Microscopy © 2017 Royal Microscopical Society
3 citations (Crossref) [2023-10-31] Citation Key Alias: jainComponentbycomponentCharacterisationHighrisk2017, lens.org/088-949-097-388-885 tex.type: [object Object]
Keywords: Aorta; Article; Atherosclerotic plaque; Calcinosis; Cholesterol; Collagen; Coronary Vessels; Hemorrhage; Humans; Image Processing; Computer-Assisted; Macrophages; Nonlinear Optical Microscopy; Plaque; Atherosclerotic; aorta; atherosclerotic plaque; autofluorescence; autopsy; bleeding; calcinosis; cholesterol; clinical article; collagen; coronary artery; coronary artery calcification; coronary blood vessel; dept.phy; ex vivo; ex vivo study; feasibility study; histopathology; human; human tissue; image processing; immunology; macrophage; macrophages; multiphoton microscopy; nonlinear optical microscopy; pathology; priority journal; procedures

Resonance raman of BCC and normal skin - Liu, Cheng-hui; Sriramoju, Vidyasagar; Boydston-White, Susie; Wu, Binlin; Zhang, Chunyuan; Pei, Zhe; Sordillo, Laura; M.d, Hugh Beckman; Alfano, Robert R. (Optical Biopsy XV: Toward Real-Time Spectroscopic Imaging and Diagnosis, 2017)
The Resonance Raman (RR) spectra of basal cell carcinoma (BCC) and normal human skin tissues were analyzed using 532nm laser excitation. RR spectral differences in vibrational fingerprints revealed skin normal and cancerous states tissues. The standard diagnosis criterion for BCC tissues are created by native RR biomarkers and its changes at peak intensity. The diagnostic algorithms for the classification of BCC and normal were generated based on SVM classifier and PCA statistical method. These statistical methods were used to analyze the RR spectral data collected from skin tissues, yielding a diagnostic sensitivity of 98.7% and specificity of 79% compared with pathological reports.
7 citations (Crossref) [2023-10-31] Citation Key Alias: lens.org/015-837-955-575-755, pop00046 tex.type: [object Object]
Keywords: Basal cell carcinoma; Basel cell carcinoma (BCC); Biopsy; Cell carcinoma; Diagnostic algorithms; Histology; Human skin; Human skin tissues; Laser excitation; Principal component analysis; Principal component analysis (PCA); Resonance; Resonance Raman spectra; Resonance Raman spectroscopy; Skin cancer; Skin cancers; Spectral differences; Statistical methods; Support vector machine (SVM); Support vector machines; Tissue; Tissue engineering; dept.phy