Designing for Divorce: New Rituls and Artifacts for an Evolving World

Our interactions with objects build cultural codes, reflecting lifestyles, values, and identities beyond functional expectations. With open connectivity in the contemporary consumer environments, we have access to homogenized material cultures not only for daily activities but also for ceremonies and rituals to mark important events, such as birth, marriage, and death. What will happen to our cultural codes and diverse traditions when various cultural norms meet, exchange, clash, hybridize, and evolve? In this research, globalized material cultures were investigated to discover metaphoric comparisons, to formulate conceptual frameworks, and to develop informed design, which can address evolving cultural conditions appropriately, in comparison with commercialized goods. Considering we often ritualize sequential stages of life course or challenging events, but rarely divorce, I explored the socio-cultural norms of marriage and divorce in the current social construct to anticipate globally evolving divorce phenomena. My thesis focused on relatively unknown material cultures in ritualizing divorce by combining speculative design with semiotic, hybrid, idiosyncratic approaches to communicate desirable future scenarios for the emerging multi-cultural context. This research aims to explore how artifacts and rituals can help people cope with transitional events and how design practices can provide meaningful and reflective material cultures

Derivation of an electronic frailty index for predicting short-term mortality in heart failure: a machine learning approach

Aims Frailty may be found in heart failure patients especially in the elderly and is associated with a poor prognosis. However, assessment of frailty status is time-consuming, and the electronic frailty indices developed using health records have served as useful surrogates. We hypothesized that an electronic frailty index developed using machine learning can improve short-term mortality prediction in patients with heart failure. Methods and results This was a retrospective observational study that included patients admitted to nine public hospitals for heart failure from Hong Kong between 2013 and 2017. Age, sex, variables in the modified frailty index, Deyo's Charlson co-morbidity index (≥2), neutrophil-to-lymphocyte ratio (NLR), and prognostic nutritional index at baseline were analysed. Gradient boosting, which is a supervised sequential ensemble learning algorithm with weak prediction submodels (typically decision trees), was applied to predict mortality. Variables were ranked in the order of importance with a total score of 100 and used to build the frailty models. Comparisons were made with decision tree and multivariable logistic regression. A total of 8893 patients (median: age 81, Q1–Q3: 71–87 years old) were included, in whom 9% had 30 day mortality and 17% had 90 day mortality. Prognostic nutritional index, age, and NLR were the most important variables predicting 30 day mortality (importance score: 37.4, 32.1, and 20.5, respectively) and 90 day mortality (importance score: 35.3, 36.3, and 14.6, respectively). Gradient boosting significantly outperformed decision tree and multivariable logistic regression. The area under the curve from a five-fold cross validation was 0.90 for gradient boosting and 0.87 and 0.86 for decision tree and logistic regression in predicting 30 day mortality. For the prediction of 90 day mortality, the area under the curve was 0.92, 0.89, and 0.86 for gradient boosting, decision tree, and logistic regression, respectively. Conclusions The electronic frailty index based on co-morbidities, inflammation, and nutrition information can readily predict mortality outcomes. Their predictive performances were significantly improved by gradient boosting techniques

Atomic-scale structure and properties of highly stable antiphase boundary defects in Fe3O4

The complex and intriguing properties of the ferrimagnetic half metal magnetite (Fe3O4) are of continuing fundamental interest as well as being important for practical applications in spintronics, magnetism, catalysis and medicine. There is considerable speculation concerning the role of the ubiquitous antiphase boundary (APB) defects in magnetite, however, direct information on their structure and properties has remained challenging to obtain. Here we combine predictive first principles modelling with high-resolution transmission electron microscopy to unambiguously determine the three-dimensional structure of APBs in magnetite. We demonstrate that APB defects on the {110} planes are unusually stable and induce antiferromagnetic coupling between adjacent domains providing an explanation for the magnetoresistance and reduced spin polarization often observed. We also demonstrate how the high stability of the {110} APB defects is connected to the existence of a metastable bulk phase of Fe3O4, which could be stabilized by strain in films or nanostructures

Clinical Implication of Targeting of Cancer Stem Cells

The existence of cancer stem cells (CSCs) is receiving increasing interest particularly due to its potential ability to enter clinical routine. Rapid advances in the CSC field have provided evidence for the development of more reliable anticancer therapies in the future. CSCs typically only constitute a small fraction of the total tumor burden; however, they harbor self-renewal capacity and appear to be relatively resistant to conventional therapies. Recent therapeutic approaches aim to eliminate or differentiate CSCs or to disrupt the niches in which they reside. Better understanding of the biological characteristics of CSCs as well as improved preclinical and clinical trials targeting CSCs may revolutionize the treatment of many cancers. Copyright (c) 2012 S. Karger AG, Base

X-Ray Spectroscopy of Stars

(abridged) Non-degenerate stars of essentially all spectral classes are soft X-ray sources. Low-mass stars on the cooler part of the main sequence and their pre-main sequence predecessors define the dominant stellar population in the galaxy by number. Their X-ray spectra are reminiscent, in the broadest sense, of X-ray spectra from the solar corona. X-ray emission from cool stars is indeed ascribed to magnetically trapped hot gas analogous to the solar coronal plasma. Coronal structure, its thermal stratification and geometric extent can be interpreted based on various spectral diagnostics. New features have been identified in pre-main sequence stars; some of these may be related to accretion shocks on the stellar surface, fluorescence on circumstellar disks due to X-ray irradiation, or shock heating in stellar outflows. Massive, hot stars clearly dominate the interaction with the galactic interstellar medium: they are the main sources of ionizing radiation, mechanical energy and chemical enrichment in galaxies. High-energy emission permits to probe some of the most important processes at work in these stars, and put constraints on their most peculiar feature: the stellar wind. Here, we review recent advances in our understanding of cool and hot stars through the study of X-ray spectra, in particular high-resolution spectra now available from XMM-Newton and Chandra. We address issues related to coronal structure, flares, the composition of coronal plasma, X-ray production in accretion streams and outflows, X-rays from single OB-type stars, massive binaries, magnetic hot objects and evolved WR stars.Comment: accepted for Astron. Astrophys. Rev., 98 journal pages, 30 figures (partly multiple); some corrections made after proof stag

Time series modeling for syndromic surveillance

BACKGROUND: Emergency department (ED) based syndromic surveillance systems identify abnormally high visit rates that may be an early signal of a bioterrorist attack. For example, an anthrax outbreak might first be detectable as an unusual increase in the number of patients reporting to the ED with respiratory symptoms. Reliably identifying these abnormal visit patterns requires a good understanding of the normal patterns of healthcare usage. Unfortunately, systematic methods for determining the expected number of (ED) visits on a particular day have not yet been well established. We present here a generalized methodology for developing models of expected ED visit rates. METHODS: Using time-series methods, we developed robust models of ED utilization for the purpose of defining expected visit rates. The models were based on nearly a decade of historical data at a major metropolitan academic, tertiary care pediatric emergency department. The historical data were fit using trimmed-mean seasonal models, and additional models were fit with autoregressive integrated moving average (ARIMA) residuals to account for recent trends in the data. The detection capabilities of the model were tested with simulated outbreaks. RESULTS: Models were built both for overall visits and for respiratory-related visits, classified according to the chief complaint recorded at the beginning of each visit. The mean absolute percentage error of the ARIMA models was 9.37% for overall visits and 27.54% for respiratory visits. A simple detection system based on the ARIMA model of overall visits was able to detect 7-day-long simulated outbreaks of 30 visits per day with 100% sensitivity and 97% specificity. Sensitivity decreased with outbreak size, dropping to 94% for outbreaks of 20 visits per day, and 57% for 10 visits per day, all while maintaining a 97% benchmark specificity. CONCLUSIONS: Time series methods applied to historical ED utilization data are an important tool for syndromic surveillance. Accurate forecasting of emergency department total utilization as well as the rates of particular syndromes is possible. The multiple models in the system account for both long-term and recent trends, and an integrated alarms strategy combining these two perspectives may provide a more complete picture to public health authorities. The systematic methodology described here can be generalized to other healthcare settings to develop automated surveillance systems capable of detecting anomalies in disease patterns and healthcare utilization

Distinctions in gastric cancer gene expression signatures derived from laser capture microdissection versus histologic macrodissection

<p>Abstract</p> <p>Background</p> <p>Gastric cancer samples obtained by histologic macrodissection contain a relatively high stromal content that may significantly influence gene expression profiles. Differences between the gene expression signature derived from macrodissected gastric cancer samples and the signature obtained from isolated gastric cancer epithelial cells from the same biopsies using laser-capture microdissection (LCM) were evaluated for their potential experimental biases.</p> <p>Methods</p> <p>RNA was isolated from frozen tissue samples of gastric cancer biopsies from 20 patients using both histologic macrodissection and LCM techniques. RNA from LCM was subject to an additional round of T7 RNA amplification. Expression profiling was performed using Affymetrix HG-U133A arrays. Genes identified in the expression signatures from each tissue processing method were compared to the set of genes contained within chromosomal regions found to harbor copy number aberrations in the tumor samples by array CGH and to proteins previously identified as being overexpressed in gastric cancer.</p> <p>Results</p> <p>Genes shown to have increased copy number in gastric cancer were also found to be overexpressed in samples obtained by macrodissection (LS <it>P </it>value < 10^-5), but not in array data generated using microdissection. A set of 58 previously identified genes overexpressed in gastric cancer was also enriched in the gene signature identified by macrodissection (LS <it>P </it>< 10^-5), but not in the signature identified by microdissection (LS <it>P </it>= 0.013). In contrast, 66 genes previously reported to be underexpressed in gastric cancer were enriched in the gene signature identified by microdissection (LS <it>P </it>< 10^-5), but not in the signature identified by macrodissection (LS <it>P </it>= 0.89).</p> <p>Conclusions</p> <p>The tumor sampling technique biases the microarray results. LCM may be a more sensitive collection and processing method for the identification of potential tumor suppressor gene candidates in gastric cancer using expression profiling.</p

Early development of spasticity following stroke: a prospective, observational trial

This study followed a cohort of 103 patients at median 6 days, 6 and 16 weeks after stroke and recorded muscle tone, pain, paresis, Barthel Index and quality of life score (EQ-5D) to identify risk-factors for development of spasticity. 24.5% of stroke victims developed an increase of muscle tone within 2 weeks after stroke. Patients with spasticity had significantly higher incidences of pain and nursing home placement and lower Barthel and EQ-5D scores than patients with normal muscle tone. Early predictive factors for presence of severe spasticity [modified Ashworth scale score (MAS) ≥3] at final follow-up were moderate increase in muscle tone at baseline and/or first follow-up (MAS = 2), low Barthel Index at baseline, hemispasticity, involvement of more than two joints at first follow-up, and paresis at any assessment point. The study helps to identify patients at highest risk for permanent and severe spasticity, and advocates for early treatment in this group