Reproductive Health as a Human Right: A Matter of Access or Provision?

This article seeks to understand why, despite over three decades of claiming women's reproductive health as a human right, we have seen little progress in reducing their health inequalities and poor health outcomes. I argue that one reason for this lack of progress may be due to a failure to clearly articulate the responsibilities of key actors, crucially states, in ensuring that women have access to, and provision of, services required to realize their reproductive rights. What is needed, this article suggests, is a framework that can translate decades of rights language into action and specifically identify the provisions required to address women's health.

The Healthy Trends of International Relations Research

As the end of the Cold War approached in 1989, Caroline Thomas argued: “It is important that the discipline [International Relations, IR] should address the issue of disease and more broadly, health, not simply to facilitate containment of disease transmission across international borders but also because central notions of justice, equity, efficiency and order are involved” (1989:273).1 Ten years later, Craig Murphy echoed these sentiments. Murphy (2001: 352) proposed that IR had yet to grapple with the political consequences of growing inequality between the world’s rich and poor, and areas such as health—where these inequalities were most stark—should become the field’s core business. How IR’s theories and methods would approach these issues was less clear. Bettcher and Yach (1998) cautioned that IR would be unable to develop progressive research projects that explored global health diplomacy as a global public good without adopting new perspectives and methods. Others warned that the expansion of security studies into areas such as global health would weaken the intellectual coherency of the field (Walt 1991:213). Taking its cue from the recent Ng and Prah Ruger (2011) study, this paper returns to these concerns to briefly explore key trends and potential future concerns of research in IR on health...

The slumbarumba jumbuck problem

The potential for simple linear relationships arising from a computer game to build student modelling and "world problem" skills is explored. The fundamental capability of the spreadsheet to tabulate and graph possible solutions is used to lay bare the problem structure for the students.

PRMT2 and RORγ expression are associated with breast cancer survival outcomes

Protein arginine methyltransferases (PRMTs) methylate arginine residues on histones and target transcription factors that play critical roles in many cellular processes, including gene transcription, mRNA splicing, proliferation, and differentiation. Recent studies have linked PRMT-dependent epigenetic marks and modifications to carcinogenesis and metastasis in cancer. However, the role of PRMT2-dependent signaling in breast cancer remains obscure. We demonstrate PRMT2 mRNA expression was significantly decreased in breast cancer relative to normal breast. Gene expression profiling, Ingenuity and protein-protein interaction network analysis after PRMT2-short interfering RNA transfection into MCF-7 cells, revealed that PRMT2-dependent gene expression is involved in cell-cycle regulation and checkpoint control, chromosomal instability, DNA repair, and carcinogenesis. For example, PRMT2 depletion achieved the following: 1) increased p21 and decreased cyclinD1 expression in (several) breast cancer cell lines, 2) decreased cell migration, 3) induced an increase in nucleotide excision repair and homologous recombination DNA repair, and 4) increased the probability of distance metastasis free survival (DMFS). The expression of PRMT2 and retinoid-related orphan receptor-γ (RORγ) is inversely correlated in estrogen receptor-positive breast cancer and increased RORγ expression increases DMFS. Furthermore, we found decreased expression of the PRMT2-dependent signature is significantly associated with increased probability of DMFS. Finally, weighted gene coexpression network analysis demonstrated a significant correlation between PRMT2-dependent genes and cell-cycle checkpoint, kinetochore, and DNA repair circuits. Strikingly, these PRMT2-dependent circuits are correlated with pan-cancer metagene signatures associated with epithelial-mesenchymal transition and chromosomal instability. This study demonstrates the role and significant correlation between a histone methyltransferase (PRMT2)-dependent signature, RORγ, the cell-cycle regulation, DNA repair circuits, and breast cancer survival outcomes.

Relationship between expectation management and client retention in online Cognitive Behavioural Therapy

Background Engaging clients from the outset of psychotherapy is important for therapeutic success. However, there is little research evaluating therapists’ initial attempts to engage clients. This article reports retrospective analysis of data from a trial of online Cognitive Behavioural Therapy (CBT) for depression. Qualitative and quantitative methods were used to evaluate how therapists manage clients’ expectations at the outset of therapy and its relationship with client retention in the therapeutic intervention. Aims To develop a system to codify expectation management in initial sessions of online CBT and evaluate its relationship with retention. Method Initial qualitative research using conversation analysis identified three different communication practices used by therapists at the start of first sessions: no expectation management, some expectation management, and comprehensive expectation management. These findings were developed into a coding scheme that enabled substantial inter-rater agreement (weighted Kappa = 0.78; 95% CI: 0.52 to 0.94) and was applied to all trial data. Results Adjusting for a range of client variables, primary analysis of data from 147 clients found comprehensive expectation management was associated with clients remaining in therapy for 1.4 sessions longer than those who received no expectation management (95% CI: -0.2 to 3.0). This finding was supported by a sensitivity analysis including an additional 21 clients (1.6 sessions, 95% CI: 0.2 to 3.1). Conclusions Using a combination of qualitative and quantitative methods, this study suggests a relationship between expectation management and client retention in online CBT for depression, which has implications for professional practice. A larger prospective study would enable a more precise estimate of retention.

Short utterance PLDA speaker verification using SN-WLDA and variance modelling techniques

This paper proposes a combination of source-normalized weighted linear discriminant analysis (SN-WLDA) and short utterance variance (SUV) PLDA modelling to improve the short utterance PLDA speaker verification. As short-length utterance i-vectors vary with the speaker, session variations and phonetic content of the utterance (utterance variation), a combined approach of SN-WLDA projection and SUV PLDA modelling is used to compensate the session and utterance variations. Experimental studies have found that a combination of SN-WLDA and SUV PLDA modelling approach shows an improvement over baseline system (WCCN[LDA]-projected Gaussian PLDA (GPLDA)) as this approach effectively compensates the session and utterance variations.

Productive inefficiency analysis and toxic chemical substances in US and Japanese manufacturing sectors

Corporate social responsibility is imperative for manufacturing companies to achieve sustainable development. Under a strong environmental information disclosure system, polluting companies are disadvantaged in terms of market competitiveness, because they lack an environmentally friendly image. The objective of this study is to analyze productive inefficiency change in relation to toxic chemical substance emissions for the United States and Japan and their corresponding policies. We apply the weighted Russell directional distance model to measure companies productive inefficiency, which represents their production technology. The data encompass 330 US manufacturing firms observed from 1999 to 2007, and 466 Japanese manufacturing firms observed from 2001 to 2008. The article focuses on nine high-pollution industries (rubber and plastics; chemicals and allied products; paper and pulp; steel and non-ferrous metal; fabricated metal; industrial machinery; electrical products; transportation equipment; precision instruments) categorized into two industry groups: basic materials industries and processing and assembly industries. The results show that productive inefficiency decreased in all industrial sectors in the United States and Japan from 2001 to 2007. In particular, that of the electrical products industry decreased rapidly after 2002 for both countries, possibly because of the enforcement of strict environmental regulations for electrical products exported to European markets.

Determinants of eco-efficiency in the Chinese industrial sector

This study measures productive inefficiency within the context of multi-environmental pollution (eco-efficiency) in the Chinese industrial sector. The weighted Russell directional distance model is applied to measure eco-efficiency using production technology. The objective is to clarify how external factors affect eco-efficiency. The major findings are that both foreign direct investment and investment for pollution abatement improve eco-efficiency as measured by air pollutant substances. A levy system for wastewater discharge improves eco-efficiency as measured by wastewater pollutant substances. However, an air pollutant levy does not significantly affect eco-efficiency as measured by air pollutants.

A normal-distribution based reputation model

Rating systems are used by many websites, which allow customers to rate available items according to their own experience. Subsequently, reputation models are used to aggregate available ratings in order to generate reputation scores for items. A problem with current reputation models is that they provide solutions to enhance accuracy of sparse datasets not thinking of their models performance over dense datasets. In this paper, we propose a novel reputation model to generate more accurate reputation scores for items using any dataset; whether it is dense or sparse. Our proposed model is described as a weighted average method, where the weights are generated using the normal distribution. Experiments show promising results for the proposed model over state-of-the-art ones on sparse and dense datasets.

A rating aggregation method for generating product reputations

Many websites offer the opportunity for customers to rate items and then use customers' ratings to generate items reputation, which can be used later by other users for decision making purposes. The aggregated value of the ratings per item represents the reputation of this item. The accuracy of the reputation scores is important as it is used to rank items. Most of the aggregation methods didn't consider the frequency of distinct ratings and they didn't test how accurate their reputation scores over different datasets with different sparsity. In this work we propose a new aggregation method which can be described as a weighted average, where weights are generated using the normal distribution. The evaluation result shows that the proposed method outperforms state-of-the-art methods over different sparsity datasets.

Utilizing voting systems for ranking user tweets

Twitter is a very popular social network website that allows users to publish short posts called tweets. Users in Twitter can follow other users, called followees. A user can see the posts of his followees on his Twitter profile home page. An information overload problem arose, with the increase of the number of followees, related to the number of tweets available in the user page. Twitter, similar to other social network websites, attempts to elevate the tweets the user is expected to be interested in to increase overall user engagement. However, Twitter still uses the chronological order to rank the tweets. The tweets ranking problem was addressed in many current researches. A sub-problem of this problem is to rank the tweets for a single followee. In this paper we represent the tweets using several features and then we propose to use a weighted version of the famous voting system Borda-Count (BC) to combine several ranked lists into one. A gradient descent method and collaborative filtering method are employed to learn the optimal weights. We also employ the Baldwin voting system for blending features (or predictors). Finally we use the greedy feature selection algorithm to select the best combination of features to ensure the best results.

A national satellite-based land-use regression model for air pollution exposure assessment in Australia

Land-use regression (LUR) is a technique that can improve the accuracy of air pollution exposure assessment in epidemiological studies. Most LUR models are developed for single cities, which places limitations on their applicability to other locations. We sought to develop a model to predict nitrogen dioxide (NO2) concentrations with national coverage of Australia by using satellite observations of tropospheric NO2 columns combined with other predictor variables. We used a generalised estimating equation (GEE) model to predict annual and monthly average ambient NO2 concentrations measured by a national monitoring network from 2006 through 2011. The best annual model explained 81% of spatial variation in NO2 (absolute RMS error=1.4 ppb), while the best monthly model explained 76% (absolute RMS error=1.9 ppb). We applied our models to predict NO2 concentrations at the ~350,000 census mesh blocks across the country (a mesh block is the smallest spatial unit in the Australian census). National population-weighted average concentrations ranged from 7.3 ppb (2006) to 6.3 ppb (2011). We found that a simple approach using tropospheric NO2 column data yielded models with slightly better predictive ability than those produced using a more involved approach that required simulation of surface-to-column ratios. The models were capable of capturing within-urban variability in NO2, and offer the ability to estimate ambient NO2 concentrations at monthly and annual time scales across Australia from 2006–2011. We are making our model predictions freely available for research.

Biological monitoring and Biological Limit Values (BLV): the strategy of the European Union

Occupational standards concerning allowable concentrations of chemical compounds in the ambient air of workplaces have been established in several countries worldwide. With the integration of the European Union (EU), there has been a need of establishing harmonised Occupational Exposure Limits (OEL). The European Commission Directive 95/320/EC of 12 July 1995 has given the tasks to a Scientific Committee for Occupational Exposure Limits (SCOEL) to propose, based on scientific data and where appropriate, occupational limit values which may include the 8-h time-weighted average (TWA), short-term limits/excursion limits (STEL) and Biological Limit Values (BLVs). In 2000, the European Union issued a list of 62 chemical substances with Occupational Exposure Limits. Of these, 25 substances received a "skin" notation, indicating that toxicologically significant amounts may be taken up via the skin. For such substances, monitoring of concentrations in ambient air may not be sufficient, and biological monitoring strategies appear of potential importance in the medical surveillance of exposed workers. Recent progress has been made with respect to formulation of a strategy related to health-based BLVs.

Score-level multibiometric fusion based on Dempster-Shafer theory incorporating uncertainty factors

While existing multi-biometic Dempster-Shafer the- ory fusion approaches have demonstrated promising perfor- mance, they do not model the uncertainty appropriately, sug- gesting that further improvement can be achieved. This research seeks to develop a unified framework for multimodal biometric fusion to take advantage of the uncertainty concept of Dempster- Shafer theory, improving the performance of multi-biometric authentication systems. Modeling uncertainty as a function of uncertainty factors affecting the recognition performance of the biometric systems helps to address the uncertainty of the data and the confidence of the fusion outcome. A weighted combination of quality measures and classifiers performance (Equal Error Rate) are proposed to encode the uncertainty concept to improve the fusion. We also found that quality measures contribute unequally to the recognition performance, thus selecting only significant factors and fusing them with a Dempster-Shafer approach to generate an overall quality score play an important role in the success of uncertainty modeling. The proposed approach achieved a competitive performance (approximate 1% EER) in comparison with other Dempster-Shafer based approaches and other conventional fusion approaches.