Lc-ms/ms method development for quantitation of nicotine in toenails as a biomaker for secondhand smoke and standard lipoprotein mimetic models

Passive smoke or (secondhand smoke) is defined as when a non-smoker is unintentionally exposed to a smoking environment from cigarettes, cigars, or pipes. Passive smoke can result in adverse health effects leading to heart disease, asthma attacks, lung cancer, and other major diseases. Smoke from active smokers has been extensively investigated by a number of researchers. These studies have examined methods for the analysis of nicotine and its metabolites. In contrast, the development of methods to follow nicotine and its metabolites in those exposed to passive or secondhand smoke, is lacking. Here we present a method developed for the determination of nicotine in toenails. We will describe a method that involves the pretreatment of toenails, followed by a liquid-liquid extraction. The extract is then analyzed by reverse phase high performance liquid chromatography (HPLC) – ion trap mass spectrometry. Some of the figures of merit for this method include quantification of the nicotine concentration level, standard curve linearity (R2 > 0.99), limit of detection (LOD = 0.005 ng/mg at m/z 163), and limit of quantitation (LOQ = 0.08 ng/mg), over the concentration range of 0.08 to 20 ng/mg. Toenail samples were individually collected for research purposes, including a non-smoker never exposed to secondhand smoke, non-smoker exposed to secondhand smoke, and an active smoker. The results indicted mean of nicotine content in non-exposed, exposed, and active smoker toenails samples are 0.103, 0.415, and 1.75 ng/mg respectively. This study also compared a solid phase extraction method. As a complex of globular proteins, lipoproteins, plays an essential role in the transport and metabolism of cholesterol. The level of several metabolites in blood are controlled by several mechanisms due to its profile. Development of common assays for lipoproteins have resulted in detection of abnormalities and can help physicians assess tissue injuries and disordering in early stages. Natural lipoprotein analysis related to cardiovascular disease is challenging even when utilizing modern analytical instrumentation. In this study, we developed mimetic lipoprotein models and characterize them using UV-Vis and fluorescence spectrophotometry to gain a better understanding of lipoproteins. An independent assay, the Amplex Red Cholesterol Assay, was also performed and used to support the mimetic lipoprotein model used in this study. Cardiovascular health is associated with different classes of lipoproteins and the composition of each component in lipoproteins. This study demonstrated that the carbon-carbon double bond of cholesterol (1668 cm-1) and the peptide backbone of tyrosine resonance are enhanced in deep ultraviolet resonance Raman (dUVRR) spectra (851, 1171, 1205, 1266, 1596, and 1615 cm-1). The excitation of wavelength 197 nm characterized features of mimetic lipoprotein models. Other measurements, such as circular dichroism (CD), UV-vis, and fluorescence spectroscopy provided spectroscopic information to identity and characterize the mimetic lipoprotein models

Credit Risk Modeling without Sensitive Features: An Adversarial Deep Learning Model for Fairness and Profit

We propose an adversarial deep learning model for credit risk modeling. We make use of sophisticated machine learning model’s ability to triangulate (i.e., infer the sensitive group affiliation by using only permissible features), which is often deemed “troublesome” in fair machine learning research, in a positive way to increase both borrower welfare and lender profits while improving fairness. We train and test our model on a dataset from a real-world microloan company. Our model significantly outperforms regular deep neural networks without adversaries and the most popular credit risk model XGBoost, in terms of both improving borrowers’ welfare and lenders’ profits. Our empirical findings also suggest that the traditional AUC metric cannot reflect a model\u27s performance on the borrowers’ welfare and lenders’ profits. Our framework is ready to be customized for other microloan firms, and can be easily adapted to many other decision-making scenarios

The Role of r-Modes in Pulsar Spindown, Pulsar Timing and Gravitational Waves

Pulsars are fast spinning neutron stars that lose their rotational energy via various processes such as gravitational and magnetic radiation, particle acceleration and mass loss processes. This dissipation can be quantified by a spin-down equation that measures the rate of change of the frequency as a function of the rotational frequency itself. We explore the pulsar spin-down and consider the spin-down equation upto the seventh order in frequency. This seventh order term accounts for energy loss due to the gravitational radiation caused by a current type quadrupole in the pulsar due to r-modes. We derive the rotational frequency due to the r-modes and find a solution in terms of the Lambert function. We also present an analytic exact solution for the period from the spindown equation and numerically verify this for the Crab pulsar. This analysis will be relevant for the detection of continuous gravitational waves by 3G ground based and space based gravitational wave detectors

Genetic Diversity and Population Differentiation of Pinus koraiensis in China

Pinus koraiensis is a well-known precious tree species in East Asia with high economic, ornamental and ecological value. More than fifty percent of the P. koraiensis forests in the world are distributed in northeast China, a region with abundant germplasm resources. However, these natural P. koraiensis sources are in danger of genetic erosion caused by continuous climate changes, natural disturbances such as wildfire and frequent human activity. Little work has been conducted on the population genetic structure and genetic differentiation of P. koraiensis in China because of the lack of genetic information. In this study, 480 P. koraiensis individuals from 16 natural populations were sampled and genotyped. Fifteen polymorphic expressed sequence tag-simple sequence repeat (EST-SSR) markers were used to evaluate genetic diversity, population structure and differentiation in P. koraiensis. Analysis of molecular variance (AMOVA) of the EST-SSR marker data showed that 33% of the total genetic variation was among populations and 67% was within populations. A high level of genetic diversity was found across the P. koraiensis populations, and the highest levels of genetic diversity were found in HH, ZH, LS and TL populations. Moreover, pairwise Fst values revealed significant genetic differentiation among populations (mean Fst = 0.177). According to the results of the STRUCTURE and Neighbor-joining (NJ) tree analyses and principal component analysis (PCA), the studied geographical populations cluster into two genetic clusters: cluster 1 from Xiaoxinganling Mountains and cluster 2 from Changbaishan Mountains. These results are consistent with the geographical distributions of the populations. The results provide new genetic information for future genome-wide association studies (GWAS), marker-assisted selection (MAS) and genomic selection (GS) in natural P. koraiensis breeding programs and can aid the development of conservation and management strategies for this valuable conifer species

The Role of rr-Modes in Pulsar Spindown, Pulsar Timing and Gravitational Waves

Pulsars are fast spinning neutron stars that lose their rotational energy via various processes such as gravitational and magnetic radiation, particle acceleration and mass loss processes. This dissipation can be quantified by a spin-down equation that measures the rate of change of the frequency as a function of the rotational frequency itself. We explore the pulsar spin-down and consider the spin-down equation upto the seventh order in frequency. This seventh order term accounts for energy loss due to the gravitational radiation caused by a current type quadrupole in the pulsar due to $r$-modes. We derive the rotational frequency due to the $r$-modes and find a solution in terms of the Lambert function. We also present an analytic exact solution for the period from the spindown equation and numerically verify this for the Crab pulsar. This analysis will be relevant for the detection of continuous gravitational waves by 3G ground based and space based gravitational wave detectors

Deep Learning in Predicting Real Estate Property Prices: A Comparative Study

The dominant methods for real estate property price prediction or valuation are multi-regression based. Regression-based methods are, however, imperfect because they suffer from issues such as multicollinearity and heteroscedasticity. Recent years have witnessed the use of machine learning methods but the results are mixed. This paper introduces the application of a new approach using deep learning models to real estate property price prediction. The paper uses a deep learning approach for modeling to improve the accuracy of real estate property price prediction with data representing sales transactions in a large metropolitan area. Three deep learning models, LSTM, GRU and Transformer, are created and compared with other machine learning and traditional models. The results obtained for the data set with all features clearly show that the RF and Transformer models outperformed the other models. LSTM and GRU models produced the worst results, suggesting that they are perhaps not suitable to predict the real estate price. Furthermore, the implementations of Transformer and RF on a data set with feature reduction produced even more accurate prediction results. In conclusion, our research shows that the performance of the Transformer model is close to the RF model. Both models produce significantly better prediction results than existing approaches in terms of accuracy