The longer your work hours, the worse your relationship? The role of selective optimization with compensation in the associations of working time with relationship satisfaction and self-disclosure in dual-career couples

This two-wave panel study investigates the associations between working time, selective optimization with compensation in private life and relationship outcomes (i.e. relationship satisfaction and self-disclosure) in dual-career couples. We propose that one partner’s selective optimization with compensation in private life either mediates or moderates the association of this partner’s working time and relationship outcomes (i.e. relationship satisfaction and self-disclosure). Moreover, we postulate the crossover (i.e. transmission) of relationship satisfaction and self-disclosure within the couple. To test these hypotheses, we conducted an online study with a time lag of six months, in which 285 dual-career couples took part. We found evidence for selective optimization with compensation in private life as a mediator: working time spent by partners in dual-career couples was associated with selective optimization with compensation in their private life that, in turn, predicted relationship satisfaction and self-disclosure. Results did not support the assumption that one partner’s selective optimization with compensation in private life moderates the association between working time and relationship satisfaction and self-disclosure. Relationship satisfaction, but not self-disclosure, crossed over within the couples. The results challenge the assumption that longer work hours have negative consequences for romantic relationships

Tissue factor/FVIIa activates Bcl-2 and prevents doxorubicin-induced apoptosis in neuroblastoma cells

<p>Abstract</p> <p>Background</p> <p>Tissue factor (TF) is a transmembrane protein that acts as a receptor for activated coagulation factor VII (FVIIa), initiating the coagulation cascade. Recent studies demonstrate that expression of tumor-derived TF also mediates intracellular signaling relevant to tumor growth and apoptosis. Our present study investigates the possible mechanism by which the interaction between TF and FVIIa regulates chemotherapy resistance in neuroblastoma cell lines.</p> <p>Methods</p> <p>Gene and siRNA transfection was used to enforce TF expression in a TF-negative neuroblastoma cell line and to silence endogenous TF expression in a TF-overexpressing neuroblastoma line, respectively. The expression of TF, Bcl-2, STAT5, and Akt as well as the phosphorylation of STAT5 and Akt in gene transfected cells or cells treated with JAK inhibitor and LY294002 were determined by Western blot assay. Tumor cell growth was determined by a clonogenic assay. Cytotoxic and apoptotic effect of doxorubicin on neuroblastoma cell lines was analyzed by WST assay and annexin-V staining (by flow cytometry) respectively.</p> <p>Results</p> <p>Enforced expression of TF in a TF-negative neuroblastoma cell line in the presence of FVIIa induced upregulation of Bcl-2, leading to resistance to doxorubicin. Conversely, inhibition of endogenous TF expression in a TF-overexpressing neuroblastoma cell line using siRNA resulted in down-regulation of Bcl-2 and sensitization to doxorubicin-induced apoptosis. Additionally, neuroblastoma cells expressing high levels of either endogenous or transfected TF treated with FVIIa readily phosphorylated STAT5 and Akt. Using selective pharmacologic inhibitors, we demonstrated that JAK inhibitor I, but not the PI3K inhibitor LY294002, blocked the TF/FVIIa-induced upregulation of Bcl-2.</p> <p>Conclusion</p> <p>This study shows that in neuroblastoma cell lines overexpressed TF ligated with FVIIa produced upregulation of Bcl-2 expression through the JAK/STAT5 signaling pathway, resulting in resistance to apoptosis. We surmise that this TF-FVIIa pathway may contribute, at least in part, to chemotherapy resistance in neuroblastoma.</p

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur

Deterioration of Parkinson's disease during hospitalization: survey of 684 patients

Abstract Background A substantial fraction of Parkinson's disease patients deteriorate during hospitalisation, but the precise proportion and the reasons why have not been studied systematically and the focus has been on surgical wards and on Accident & Emergency departments. We assessed the prevalence and risk factors of deterioration of Parkinson's disease symptoms during hospitalization, including all wards. Methods We invited Parkinson's disease patients from three neurology departments in The Netherlands to answer a standardised questionnaire on general, disease and hospital related issues. Patients who had been hospitalized in the previous year were included and analysed. Possible risk factors for Parkinson's disease deterioration were identified. Proportions were analysed using the Chi-Square test and a logistic regression analysis was performed. Results Eighteen percent of 684 Parkinson's disease patients had been hospitalized at least once in the last year. Twenty-one percent experienced deterioration of motor symptoms, 33% did have one or more complications and 26% had received incorrect anti-Parkinson's medication. There were no statistically significant differences for these variables between admissions on neurologic or non-neurologic wards and between having surgery or not. Incorrect medication during hospitalization was significantly associated with higher risk (OR 5.8, CI 2.5-13.7) of deterioration, as were having infections (OR 6.7 CI 1.8-24.7). A higher levodopa equivalent dose per day was a significant risk factor for deterioration. When adjusting for different variables, wrong medication distribution was the most important risk factor for deterioration. Conclusions Incorrect medication and infections are the important risk factors for deterioration of Parkinson's disease patients both for admissions with and without surgery and both for admissions on neurologic and non-neurologic wards. Measures should be taken to improve care and incorporated in guidelines.</p

Intragenic DNA methylation: implications of this epigenetic mechanism for cancer research

Epigenetics is the study of all mechanisms that regulate gene transcription and genome stability that are maintained throughout the cell division, but do not include the DNA sequence itself. The best-studied epigenetic mechanism to date is DNA methylation, where methyl groups are added to the cytosine base within cytosine–guanine dinucleotides (CpG sites). CpGs are frequently clustered in high density (CpG islands (CGIs)) at the promoter of over half of all genes. Current knowledge of transcriptional regulation by DNA methylation centres on its role at the promoter where unmethylated CGIs are present at most actively transcribed genes, whereas hypermethylation of the promoter results in gene repression. Over the last 5 years, research has gradually incorporated a broader understanding that methylation patterns across the gene (so-called intragenic or gene body methylation) may have a role in transcriptional regulation and efficiency. Numerous genome-wide DNA methylation profiling studies now support this notion, although whether DNA methylation patterns are a cause or consequence of other regulatory mechanisms is not yet clear. This review will examine the evidence for the function of intragenic methylation in gene transcription, and discuss the significance of this in carcinogenesis and for the future use of therapies targeted against DNA methylation

A Differential Evolution Framework with Ensemble of Parameters and Strategies and Pool of Local Search Algorithms

The file attached to this record is the author's final peer reviewed version. The publisher's final version can be found by following the DOI link.The ensemble structure is a computational intelligence supervised strategy consisting of a pool of multiple operators that compete among each other for being selected, and an adaptation mechanism that tends to reward the most successful operators. In this paper we extend the idea of the ensemble to multiple local search logics. In a memetic fashion, the search structure of an ensemble framework cooperatively/competitively optimizes the problem jointly with a pool of diverse local search algorithms. In this way, the algorithm progressively adapts to a given problem and selects those search logics that appear to be the most appropriate to quickly detect high quality solutions. The resulting algorithm, namely Ensemble of Parameters and Strategies Differential Evolution empowered by Local Search (EPSDE-LS), is evaluated on multiple testbeds and dimensionality values. Numerical results show that the proposed EPSDE-LS robustly displays a very good performance in comparison with some of the state-of-the-art algorithms