Factors assisting breast cancer survivors improve quality of life : A salutogenic approach

Through improved technology in cancer treatment, the rate of breast cancer survivors has increased tremendously. Curiosity into the life beyond breast cancer treatment has led to this study. The aim of this study is to determine the factors that have contributed to improve quality of life in breast cancer survivors after treatment has been completed. Aaron Antonovsky's salutogenic model of health promotion was used as framework for this study. Qualitative content analysis was used in the method analysis of the study. Materials for analysis were qualitative articles. The literature used contained interviews from breast cancer survivors, telling their perception of the factors that has assisted in improving their quality of life. Results showed four categories, applying deductive approach: Support system, Selflessness, Resilience and Appreciation of life. Having a strong support system, being selfless, showing resilience and increased appreciation of life helped improve quality of life of breast cancer survivors. This study further revealed that quality of life is not a culture bound concept but rather a multidimensional and an individual concept

Virtual screening for PPAR-gamma ligands using the ISOAK molecular graph kernel and gaussian processes

For a virtual screening study, we introduce a combination of machine learning techniques, employing a graph kernel, Gaussian process regression and clustered cross-validation. The aim was to find ligands of peroxisome-proliferator activated receptor gamma (PPAR-y). The receptors in the PPAR family belong to the steroid-thyroid-retinoid superfamily of nuclear receptors and act as transcription factors. They play a role in the regulation of lipid and glucose metabolism in vertebrates and are linked to various human processes and diseases. For this study, we used a dataset of 176 PPAR-y agonists published by Ruecker et al. ..

How to Explain Individual Classification Decisions

After building a classifier with modern tools of machine learning we typically have a black box at hand that is able to predict well for unseen data. Thus, we get an answer to the question what is the most likely label of a given unseen data point. However, most methods will provide no answer why the model predicted the particular label for a single instance and what features were most influential for that particular instance. The only method that is currently able to provide such explanations are decision trees. This paper proposes a procedure which (based on a set of assumptions) allows to explain the decisions of any classification method.Comment: 31 pages, 14 figure

Tropospheric QBO-ENSO interactions and differences between Atlantic and Pacific

This study investigates the interaction of the Quasi-Biennial Oscillation (QBO) and the El Niño-Southern Oscillation (ENSO) in the troposphere separately for the North Pacific and North Atlantic region. Three 145-year model simulations with NCAR’s Community Earth Sytem Model (CESM-WACCM) are analyzed where only natural and no anthropogenic forcings are considered. These long simulations allow us to obtain statistically reliable results from an exceptional large number of cases for each combination of the QBO (westerly and easterly) and ENSO phases (El Niño and La Niña). Two different analysis methods were applied to investigate where nonlinearity might play a role in QBO-ENSO interactions. The analyses reveal that the stratospheric equatorial QBO anomalies extend down to the troposphere over the North Pacific during Northern hemisphere winter only during La Niña and not during El Niño events. The Aleutian low is deepened during QBO westerly (QBOW) as compared to QBO easterly (QBOE) conditions, and the North Pacific subtropical jet is shifted northward during La Niña. In the North Atlantic, the interaction of QBOW with La Niña conditions (QBOE with El Niño) results in a positive (negative) North Atlantic Oscillation (NAO) pattern. For both regions, nonlinear interactions between the QBO and ENSO might play a role. The results provide potential to enhance the skill of tropospheric seasonal predictions in the North Atlantic and North Pacific region

When cultures clash:Links between perceived cultural distance in values and attitudes towards migrants

Migration elicits mixed reactions from the host‐society. Negative responses towards migrants seem to emerge when migrants are perceived as culturally different. We investigated when and why perceived cultural distance (PCD) is associated with negative migrant attitudes by focussing on differences in cultural values. We expected that PCD in social values (focus on relationships and society) should be more strongly associated with attitudes towards migrants than personal values (individual needs and gains) and should be mediated by symbolic threat. In two quasi‐experimental studies (N = 200, N = 668) with Dutch participants (host‐society), we simultaneously tested effects of respondents’ perception of Dutch values, their perceptions of migrant values (of Moroccan, Syrian, Polish ethnic origin), and PCD between Dutch‐migrant value on attitudes. For all migrant groups, PCD in social values was associated with more negative attitudes, less tolerance, and less policy support regarding migrants; this was mediated by symbolic threat. These links were weaker for personal values

Multi-task learning for pKa prediction

Many compound properties depend directly on the dissociation constants of its acidic and basic groups. Significant effort has been invested in computational models to predict these constants. For linear regression models, compounds are often divided into chemically motivated classes, with a separate model for each class. However, sometimes too few measurements are available for a class to build a reasonable model, e.g., when investigating a new compound series. If data for related classes are available, we show that multi-task learning can be used to improve predictions by utilizing data from these other classes. We investigate performance of linear Gaussian process regression models (single task, pooling, and multi-task models) in the low sample size regime, using a published data set (n=698, mostly monoprotic, in aqueous solution) divided beforehand into 15 classes. A multi-task regression model using the intrinsic model of co-regionalization and incomplete Cholesky decomposition performed best in 85% of all experiments. The presented approach can be applied to estimate other molecular properties where few measurements are availabl

The influence of natural and anthropogenic factors on major stratospheric sudden warmings

Major stratospheric sudden warmings are prominent disturbances of the Northern Hemisphere polar winter stratosphere. Understanding the factors controlling major warmings is required, since the associated circulation changes can propagate down into the troposphere and affect the surface climate, suggesting enhanced prediction skill when these processes are accurately represented in models. In this study we investigate how different natural and anthropogenic factors, namely, the quasi-biennial oscillation (QBO), sea surface temperatures (SSTs), anthropogenic greenhouse gases, and ozone-depleting substances, influence the frequency, variability, and life cycle of major warmings. This is done using sensitivity experiments performed with the National Center for Atmospheric Research's Community Earth System Model (CESM). CESM is able to simulate the life cycle of major warmings realistically. The QBO strengthens the climatological stratospheric polar night jet (PNJ) and significantly reduces the frequency of major warmings through reduction of planetary wave propagation into the PNJ region. Variability in SSTs weakens the PNJ and significantly increases the major warming frequency due to enhanced wave forcing. Even extreme climate change conditions (RCP8.5 scenario) do not influence the total frequency but determine the prewarming phase of major warmings. The amplitude and duration of major warmings seem to be mainly determined by internal stratospheric variability. We also suggest that SST variability, two-way ocean/atmosphere coupling, and hence the memory of the ocean are needed to reproduce the observed tropospheric negative Northern Annular Mode pattern after major warmings

Machine Learning of Molecular Electronic Properties in Chemical Compound Space

The combination of modern scientific computing with electronic structure theory can lead to an unprecedented amount of data amenable to intelligent data analysis for the identification of meaningful, novel, and predictive structure-property relationships. Such relationships enable high-throughput screening for relevant properties in an exponentially growing pool of virtual compounds that are synthetically accessible. Here, we present a machine learning (ML) model, trained on a data base of \textit{ab initio} calculation results for thousands of organic molecules, that simultaneously predicts multiple electronic ground- and excited-state properties. The properties include atomization energy, polarizability, frontier orbital eigenvalues, ionization potential, electron affinity, and excitation energies. The ML model is based on a deep multi-task artificial neural network, exploiting underlying correlations between various molecular properties. The input is identical to \emph{ab initio} methods, \emph{i.e.} nuclear charges and Cartesian coordinates of all atoms. For small organic molecules the accuracy of such a "Quantum Machine" is similar, and sometimes superior, to modern quantum-chemical methods---at negligible computational cost

Solar forcing synchronizes decadal North Atlantic climate variability

Quasi-decadal variability in solar irradiance has been suggested to exert a substantial effect on Earth’s regional climate. In the North Atlantic sector, the 11-year solar signal has been proposed to project onto a pattern resembling the North Atlantic Oscillation (NAO), with a lag of a few years due to ocean-atmosphere interactions. The solar/NAO relationship is, however, highly misrepresented in climate model simulations with realistic observed forcings. In addition, its detection is particularly complicated since NAO quasi-decadal fluctuations can be intrinsically generated by the coupled ocean-atmosphere system. Here we compare two multi-decadal ocean-atmosphere chemistry-climate simulations with and without solar forcing variability. While the experiment including solar variability simulates a 1–2-year lagged solar/NAO relationship, comparison of both experiments suggests that the 11-year solar cycle synchronizes quasi-decadal NAO variability intrinsic to the model. The synchronization is consistent with the downward propagation of the solar signal from the stratosphere to the surface