Using Machine Learning and Natural Language Processing to Review and Classify the Medical Literature on Cancer Susceptibility Genes

PURPOSE: The medical literature relevant to germline genetics is growing exponentially. Clinicians need tools monitoring and prioritizing the literature to understand the clinical implications of the pathogenic genetic variants. We developed and evaluated two machine learning models to classify abstracts as relevant to the penetrance (risk of cancer for germline mutation carriers) or prevalence of germline genetic mutations. METHODS: We conducted literature searches in PubMed and retrieved paper titles and abstracts to create an annotated dataset for training and evaluating the two machine learning classification models. Our first model is a support vector machine (SVM) which learns a linear decision rule based on the bag-of-ngrams representation of each title and abstract. Our second model is a convolutional neural network (CNN) which learns a complex nonlinear decision rule based on the raw title and abstract. We evaluated the performance of the two models on the classification of papers as relevant to penetrance or prevalence. RESULTS: For penetrance classification, we annotated 3740 paper titles and abstracts and used 60% for training the model, 20% for tuning the model, and 20% for evaluating the model. The SVM model achieves 89.53% accuracy (percentage of papers that were correctly classified) while the CNN model achieves 88.95 % accuracy. For prevalence classification, we annotated 3753 paper titles and abstracts. The SVM model achieves 89.14% accuracy while the CNN model achieves 89.13 % accuracy. CONCLUSION: Our models achieve high accuracy in classifying abstracts as relevant to penetrance or prevalence. By facilitating literature review, this tool could help clinicians and researchers keep abreast of the burgeoning knowledge of gene-cancer associations and keep the knowledge bases for clinical decision support tools up to date

Inclination-Dependent Luminosity Function of Spiral Galaxies in the Sloan Digital Sky Survey: Implication for Dust Extinction

Using a samples of 61506 spiral galaxies selected from the SDSS DR2, we examine the luminosity function (LF) of spiral galaxies with different inclination angles. We find that the characteristic luminosity of the LF, $L^*$, decreases with increasing inclination, while the faint-end slope, $\alpha$, depends only weakly on it. The inclination-dependence of the LF is consistent with that expected from a simple model where the optical depth is proportional to the cosine of the inclination angle, and we use a likelihood method to recover both the coefficient in front of the cosine, $\gamma$, and the LF for galaxies viewed face-on. The value of $\gamma$ is quite independent of galaxy luminosity in a given band, and the values of $\gamma$ obtained in this way for the 5 SDSS bands give an extinction curve which is a power law of wavelength ($\tau\propto\lambda^{-n}$), with a power index $n=0.96\pm0.04$. Using the dust extinction for galaxies obtained by Kauffmann et al. (2003), we derive an `extinction-corrected' luminosity function for spiral galaxies. Dust extinction makes $M^*$ dimmer by about 0.5 magnitudes in the $z$-band, and about 1.2 magnitudes in the $u$- band. Since our analysis is based on a sample where selection effects are well under control, the dimming of edge-on galaxies relative to face-on galaxies is best explained by assuming that galaxy disks are optically thick in dust absorptions.Comment: 11 pages, 10 figures, accepted by Ap

Synthesis, Characterization, and Photocatalytic Activity of Zn-Doped SnO 2

Zn-doped SnO2/Zn2SnO4 nanocomposites were prepared via a two-step hydrothermal synthesis method. The as-prepared samples were characterized by X-ray diffraction (XRD), field-emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM), UV-vis diffuse reflection spectroscopy, and adsorption-desorption isotherms. The results of FESEM and TEM showed that the as-prepared Zn-doped SnO2/Zn2SnO4 nanocomposites are composed of numerous nanoparticles with the size ranging from 20 nm to 50 nm. The specific surface area of the as-prepared Zn-doped SnO2/Zn2SnO4 nanocomposites is estimated to be 71.53 m2/g by the Brunauer-Emmett-Teller (BET) method. The photocatalytic activity was evaluated by the degradation of methylene blue (MB), and the resulting showed that Zn-doped SnO2/Zn2SnO4 nanocomposites exhibited excellent photocatalytic activity due to their higher specific surface area and surface charge carrier transfer

Effect of Axial Force on the Performance of Micromachined Vibratory Rate Gyroscopes

It is reported in the published literature that the resonant frequency of a silicon micromachined gyroscope decreases linearly with increasing temperature. However, when the axial force is considerable, the resonant frequency might increase as the temperature increases. The axial force is mainly induced by thermal stress due to the mismatch between the thermal expansion coefficients of the structure and substrate. In this paper, two types of micromachined suspended vibratory gyroscopes with slanted beams were proposed to evaluate the effect of the axial force. One type was suspended with a clamped-free (C-F) beam and the other one was suspended with a clamped-clamped (C-C) beam. Their drive modes are the bending of the slanted beam, and their sense modes are the torsion of the slanted beam. The relationships between the resonant frequencies of the two types were developed. The prototypes were packaged by vacuum under 0.1 mbar and an analytical solution for the axial force effect on the resonant frequency was obtained. The temperature dependent performances of the operated mode responses of the micromachined gyroscopes were measured. The experimental values of the temperature coefficients of resonant frequencies (TCF) due to axial force were 101.5 ppm/°C for the drive mode and 21.6 ppm/°C for the sense mode. The axial force has a great influence on the modal frequency of the micromachined gyroscopes suspended with a C-C beam, especially for the flexure mode. The quality factors of the operated modes decreased with increasing temperature, and changed drastically when the micromachined gyroscopes worked at higher temperatures

Optical and Near-Infrared Color Profiles in Nearby Early-Type Galaxies and The Implied Age and Metallicity Gradients

We present results of age and metallicity gradient analysis inferred from both optical and near-infrared surface photometry. The analysis is based on a sample of 36 nearby early-type galaxies, obtained from the Early Data Release of the Sloan Digital Sky Survey and the Two Micron All Sky Survey. Surface brightness profiles were derived in each band, and used to study the color gradients of the galaxies. Using simple stellar population models with both optical and near infrared colors, we may interpret the color gradients in term of age and metallicity gradients of galaxies. Using $g_Z \equiv d \log Z_{\rm met} / d \log R$ and $g_A = d \log {\rm Age} / d \log R$ to represent the metallicity and age gradients, we found a median value of $g_Z=-0.25\pm 0.03$ for the metallicity gradient, with a dispersion $\sigma_{g_Z}=0.19\pm0.02$. The corresponding values for the age gradient were $g_A=0.02\pm 0.04$ and $\sigma_{g_A}=0.25\pm0.03$. These results are in good agreement with recent observational results, as well as with recent simulations that suggest both monolithic collapse and major merger have played important roles in the formation of early-type galaxies. Our results demonstrate the potential of using multi-waveband colors obtained from current and future optical and infrared surveys in constraining the age and metallicity gradients of early-type galaxies.Comment: 40 pages, 14 figures. Revised version. Accepted by Ap

INCIDENCE AND PROGNOSIS OF SECOND PRIMARY MALIGNANCY AMONG BREAST CANCER SURVIVORS

Second primary cancer (SPC) is a life-threatening comorbidity and a major cause of mortality among breast cancer survivors. Consistent evidence has shown that breast cancer survivors are experiencing an elevated risk of developing SPCs compared to the general population. However, it is not clear how the SPC risk changes by time since breast cancer diagnosis and there is limited data on the prognosis after SPCs among breast cancer survivors. This dissertation aimed to evaluate the risk and survival outcomes of SPCs among breast cancer survivors from a representative sample of the US population. Aim 1 examined the risk of SPCs among breast cancer survivors by follow-up time since breast cancer diagnosis as compared to the general population. We found that the risk of all SPCs combined increased with longer follow-up time since the first diagnosis, which was mainly attributed to the increasing trend in the risk of second breast cancers. The elevated risk of non-breast SPCs remained stable across follow-up time overall but varied largely according to cancer type. Hormone receptor status and treatment of the first breast cancer as well as race/ethnicity significantly modified the risk trends. Aim 2 investigated the mortality after a SPC in breast cancer survivors and compared these risks with mortality after a first primary cancer (FPC). We observed that survivors with SPCs had higher risk of cancer death and death overall than women with age matched FPCs. Increased risk of cancer death was observed for SPCs in breast, lung, colorectum, uterus, lymphoma, melanoma, thyroid, and leukemia. Previous chemotherapy contributed to a larger mortality difference between women with SPC and FPC. Aim 3 examined racial/ethnic disparities in mortality after a SPC in breast cancer survivors. We observed that Non-Hispanic Black (NHB) survivors experienced significantly higher risk of death from cancer and from cardiovascular disease (CVD) than Non-Hispanic White (NHW) survivors. The NHB-NHW disparity was larger among survivors who developed a SPC younger than 70 years. Aim 4 evaluated the performance of an online prognostic tool PREDICT in estimating the 5-year breast cancer-specific mortality for women diagnosed with a second breast cancer. The PREDICT model underestimated the mortality in women diagnosed with a second ER-positive breast cancer and in certain groups of women diagnosed with a second ER-negative cancer. This dissertation improves our understanding of the risk and prognosis of second cancers among breast cancer survivors. More tailored prevention and treatment approaches are needed to reduce the incidence of second cancer and to improve the survival for breast cancer survivors who developed a subsequent cancer

A New Combination in Mackaya (Acanthaceae), with Lectotypification for Mackaya tapingensis

Volume: 19Start Page: 307End Page: 30

Using Twitter to Better Understand the Spatiotemporal Patterns of Public Sentiment: A Case Study in Massachusetts, USA

Twitter provides a rich database of spatiotemporal information about users who broadcast their real-time opinions, sentiment, and activities. In this paper, we sought to investigate the holistic influence of land use and time period on public sentiment. A total of 880,937 tweets posted by 26,060 active users were collected across Massachusetts (MA), USA, through 31 November 2012 to 3 June 2013. The IBM Watson Alchemy API (application program interface) was employed to quantify the sentiment scores conveyed by tweets on a large scale. Then we statistically analyzed the sentiment scores across different spaces and times. A multivariate linear mixed-effects model was used to quantify the fixed effects of land use and the time period on the variations in sentiment scores, considering the clustering effect of users. The results exposed clear spatiotemporal patterns of users’ sentiment. Higher sentiment scores were mainly observed in the commercial and public areas, during the noon/evening and on weekends. Our findings suggest that social media outputs can be used to better understand the spatial and temporal patterns of public happiness and well-being in cities and regions