Suchergebnisse

Since independence, Pakistan, like many other countries, has
been facing the problem of the balance-of-payments deficit. A number of
policies have been introduced during different periods for rapid and
continuous growth in Pakistan's exports. These policies, like import
substitution, devaluation of the rupee in 1972, export finance schemes,
tax concessions, delinking of the rupee from the U.S. dollar in 1982,
etc., have helped in boosting its exports to some extent but not enough
to stabilise its export earnings. The fluctuations in export earnings
are known to have serious consequences. Specifically, unstable export
earnings affect the investment decisions by hindering the continuous
import of industrial raw materials. This, in turn, impedes the growth of
the industrial sector. Moreover, it causes fluctuations in the GNP and
promotes uncertainty in the economy. This uncertainty plays a decisive
role in the private sector's hesitation to invest in the large-scale
manufacturing industries, thereby hampering the country's overall
development. Keeping in view the possibly serious consequences of export
earnings instability, a study exploring its causes is in order.
Concentration of exports on a few commodities and exporting to only a
few markets is among the possible explanations of the current
instability in Pakistan's export earnings. Due to commodity
concentration, the chances of offsetting the impact of adverse price
movements in the international market are reduced. This commodity
concentration is often associated with the concentration on primary
products and is, therefore, the basis for a policy of diversification
away from primary products. A diversification away from primary products
and towards industrial goods is desirable for another reason, not
central to this paper; and that is that the terms of trade argument
which claims that the relative prices of the primary products have
increased slowly relative to the prices of the manufactured goods in the
international market.

This paper analyses the issue of technology adoption by
export-oriented enterprises based on survey data. Using the Rank Model
of technology adoption, the paper explores the role of several firm
level characteristics that can influence firm's decision to adopt new
technology. The results show that younger and bigger firms have a higher
probability of technology adoption. Firms that have obtained
certifications to product and process standards demonstrate a higher
likelihood of technology adoption. Domestically-owned firms are found to
have a higher probability of technology adoption as compared with
foreign-owned firms. The empirical findings underscore the need for
policy options to encourage export-oriented enterprises to adopt new
technology including, for example, fiscal incentives to encourage
research and development activities, and up-gradation of physical
infrastructure for product testing to facilitate certifications. JEL
classification: C25, L60, O14 Keywords: Manufacturing Industries, Probit
Model, Technology Adoption.

This study focuses on the Bugis ethnic identity and retention efforts, among the Bugis community in Pontian, Johor. This study used qualitative methods with ethnographic approach through semi-structured interviews, moderate participant observation and documentation. Data were analysed using NVIVO 8 software to assist in the process of transcription and data organization. NVIVO 8 generates data analysis has been prepared based on coding, themes, order and categories. The entire data have been organized checked using triangulation, data, technique and time. The results show that the Bugis ethnic identity, which is still practiced in Pontian until now, is a form of Bugis non-material culture. While maintaining the identity of the efforts made by individuals and groups by way of inheritance of cultural values through the filtering process of filtering elements of culture that is based on Islam, adaptation to local culture and government policy.

Background-Whereas heart failure (HF) is a complex clinical syndrome, conventional approaches to its management have treated it as a singular disease, leading to inadequate patient care and inefficient clinical trials. We hypothesized that applying advanced analytics to a large cohort of HF patients would improve prognostication of outcomes, identify distinct patient phenotypes, and detect heterogeneity in treatment response. Methods and Results-The Swedish Heart Failure Registry is a nationwide registry collecting detailed demographic, clinical, laboratory, and medication data and linked to databases with outcome information. We applied random forest modeling to identify predictors of 1-year survival. Cluster analysis was performed and validated using serial bootstrapping. Association between clusters and survival was assessed with Cox proportional hazards modeling and interaction testing was performed to assess for heterogeneity in response to HF pharmacotherapy across propensity-matched clusters. Our study included 44 886 HF patients enrolled in the Swedish Heart Failure Registry between 2000 and 2012. Random forest modeling demonstrated excellent calibration and discrimination for survival (C-statistic=0.83) whereas left ventricular ejection fraction did not (C-statistic=0.52): there were no meaningful differences per strata of left ventricular ejection fraction (1-year survival: 80%, 81%, 83%, and 84%). Cluster analysis using the 8 highest predictive variables identified 4 clinically relevant subgroups of HF with marked differences in 1-year survival. There were significant interactions between propensity-matched clusters (across age, sex, and left ventricular ejection fraction and the following medications: diuretics, angiotensin-converting enzyme inhibitors, )i-blockers, and nitrates, Pamp;lt;0.001, all). Conclusions-Machine learning algorithms accurately predicted outcomes in a large data set of HF patients. Cluster analysis identified 4 distinct phenotypes that differed significantly in outcomes and in response to therapeutics. Use of these novel analytic approaches has the potential to enhance effectiveness of current therapies and transform future HF clinical trials. ; Funding Agencies|Swedish Federal Government; Swedish Research Council; Swedish Heart-Lung Foundation; Stockholm County Council

Heart failure is a syndrome with a pathophysiological basis that can be traced to dysfunction in several interconnected molecular pathways. Identification of biomarkers of heart failure that allow measurement of the disease on a molecular level has resulted in enthusiasm for their use in prognostication and selection of appropriate therapies. However, despite considerable amounts of information available on numerous biomarkers, inconsistent research methodologies and lack of clinical correlations have made bench-to-bedside translations rare and left the literature with countless publications of varied quality. There is a need for a systematic and collaborative approach aimed at definitively studying the clinical benefits of novel biomarkers. In this review, on the basis of input from academia, industry, and governmental agencies, we propose a systematized approach based on adherence to specific quality measures for studies looking to augment current prediction model or use biomarkers to tailor therapeutics. We suggest that study quality, rather than results, should determine publication and propose a system for grading biomarker studies. We outline the need for collaboration between clinical investigators and statisticians to introduce more advanced statistical methodologies into the field of biomarkers that would allow for data from a large number of variables to be distilled into clinically actionable information. Lastly, we propose the creation of a heart failure biomarker consortium that would allow for a comprehensive list of biomarkers to be concomitantly analyzed in a pooled sample of randomized clinical trials and hypotheses to be generated for testing in biomarker-guided trials. Such a consortium could collaborate in sharing samples to identify biomarkers, undertake meta- analyses on completed trials, and spearhead clinical trials to test the clinical utility of new biomarkers.

To access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked Files. This article is open access. ; Protein-truncating variants protective against human disease provide in vivo validation of therapeutic targets. Here we used targeted sequencing to conduct a search for protein-truncating variants conferring protection against inflammatory bowel disease exploiting knowledge of common variants associated with the same disease. Through replication genotyping and imputation we found that a predicted protein-truncating variant (rs36095412, p.R179X, genotyped in 11,148 ulcerative colitis patients and 295,446 controls, MAF=up to 0.78%) in RNF186, a single-exon ring finger E3 ligase with strong colonic expression, protects against ulcerative colitis (overall P=6.89 × 10(-7), odds ratio=0.30). We further demonstrate that the truncated protein exhibits reduced expression and altered subcellular localization, suggesting the protective mechanism may reside in the loss of an interaction or function via mislocalization and/or loss of an essential transmembrane domain. ; National Institute of Diabetes and Digestive and Kidney Disease (NIDDK) DK064869 DK062432 National Human Genome Research Institute (NHGRI) DK064869 DK043351 HG005923 Crohns and Colitis Foundation 3765 Leona M. & Harry B. Helmsley Charitable Trust 2015PG-IBD001 Amgen 2013583217 CCFA 3765 Cedars-Sinai F. Widjaja Foundation, info:eu-repo/grantAgreement/EC/FP7/305479, European Union DK062413 AI067068 U54DE023789-01 Leona M. and Harry B. Helmsley Charitable Trust Crohn's and Colitis Foundation of America NIH DK062431 U01 DK062429 U01 DK062422 R01 DK092235 U01 DK062420 Medical Research Council, UK MR/J00314X/1 Wellcome Trust WT091310 098051 Inflammatory Bowel Disease Genetic Research Chair at the University of Pittsburgh PO1DK046763