Association of severe coronary stenosis with subclinical left ventricular dysfunction in the absence of infarction

Background. Regional left ventricular (LV) dysfunction may occur in patients with coronary artery disease (CAD) in the absence of infarction, but the causes of this phenomenon are unclear. We sought to identify whether changes in regional LV function were related to stenosis severity, using sensitive new ultrasound markers of function. Methods: We studied 67 individuals with no history of infarction and with normal LV systolic function: 49 patients with CAD and 18 control subjects without CAD. All patients underwent color Doppler tissue imaging, integrated backscatter (IB), anatomic M-mode echocardiography, and strain rate imaging to detect changes in structure and function. Peak early and late diastolic myocardial velocity, cyclic variation of IB, wall thickness, and percent wall thickening were measured in each basal and mid segment. Strain rate and peak systolic strain were calculated in each wall. CAD was defined as greater than or equal to 50% diameter stenosis. Normokinetic segments (n = 354) subtended by CAD were divided according to stenosis severity into 3 groups: group 1 (subtended by 50%-69% stenosis); group 2 (subtended by 70%-98% stenosis); and group 3 (subtended by greater than or equal to99% stenosis). Each parameter in each group was compared with that in 216 segments from control subjects. Results: Segments subtended by significant CAD showed lower peak early and late diastolic myocardial velocity compared with control segments. Group 3 showed significantly lower myocardial velocities than group 2 for both peak early (4.8 +/- 1.8 vs 6.0 +/- 2.0 cm/s, P

Does participation in a pain course based on the international association for the study of pain's curricula guidelines change student knowledge about pain?

The People in Pain course was set up as a joint initiative of the Departments of Occupational Therapy and Physiotherapy within the School of Health and Rehabilitation Sciences at The University of Queensland. It was instigated in response to the publication of Pain Curricula for Occupational Therapy and Physiotherapy by the International Association for the Study of Pain (IASP) in 1994 (1). The first year it was offered, the "People in Pain" course comprised 14 h of lecture content. It was then expanded to encompass 28 h of lectures and seminar involvement. OBJECTIVES: To evaluate the impact of participation in a university pain course that meets the IASP pain curricula guidelines to increase health professional students' knowledge about pain. METHODS: Students who participated in the People in Pain course over the first three years were invited to complete the Revised Pain Knowledge and Attitudes Questionnaire (R-PKAQ) pre- and postcourse. Data obtained from 22 students in the short course formed a pilot project, and data from 22 students in the longer version of the course were used in the present study. RESULTS: Examination of the correlation matrix indicated substantial correlations between all R-PKAQ subscales except physiological basis of pain and pharmacological management of pain. In both the pilot project during the first year of the course and the expanded course in the following two years, significant improvement was found in the students' knowledge on five of the six subscales of the R-PKAQ: physiological basis of pain, psychological factors of pain perception, assessment and measurement of pain, cognitive-behavioural methods of pain relief, and pharmacological management of pain. Improvements in the developmental aspects of pain perception subscale failed to reach significance. CONCLUSIONS: An integrated pain course developed according to the pain curriculum guidelines developed by the IASP resulted in increased student knowledge regardless of the length of the program attended.

The Brazilian Business World: the difficult adaptation to globalization

The article deals with the internationalization of Brazilian businesses in the current decade. In the 1990s, Brazil embraced economic neoliberalism and promoted a huge opening up of its economy. At that time, Brazilian companies had to adapt rapidly. Twenty years later, the country has reinforced its presence in Latin America and has ensured a better position in the global markets, especially by through agricultural exports.

On the discovery of business processes orchestration patterns

COORDINSPECTOR is a Software Tool aiming at extracting the coordination layer of a software system. Such a reverse engineering process provides a clear view of the actually invoked services as well as the logic behind such invocations. The analysis process is based on program slicing techniques and the generation of, System Dependence Graphs and Coordination Dependence Graphs. The tool analyzes Common Intermediate Language (CIL), the native language of the Microsoft .Net Framework, thus making suitable for processing systems developed in any .Net Framework compilable language. COORDINSPECTOR generates graphical representations of the coordination layer together with business process orchestrations specified in WSBPEL 2.0

Divulgação de informação sobre Activos Intangíveis, Concentrações de Actividades Empresariais e Goodwill: evidência empírica nas empresas cotadas na Bolsa de Valores Portuguesa.

This paper analyzes the level of disclosure of Intangible Assets, Business Combinations and Goodwill in the consolidated financial statements and its explanatory factors. We have analyzed the annual reports of companies listed on Euronext Lisbon for the year 2010 and have created a Disclosure Index which allows to measure the level of disclosure for each company in this area, based on the existing accounting standards. In order to identify the association between the level of disclosure and its determinants, we have tested several hypotheses which explain the degree of disclosure. The results evidences, company’s size, industry, leverage and audit firms belonging to Big 4as explanatory factors. Only the size presentsa positive association with disclosure index, confirming the hypothesis which establishes that the level of disclosure about Intangible Assets, Business Combinations and Goodwill is greater in larger companies. The remaining explanatory variables presenta negative association with disclosure level.

The perceived potential of business social networking sites

Business social networking is a facilitator of several business activities, such as market studies, communication with clients, and identification of business partners. This paper traduces the results of a study undertaken with the purpose of getting to know how the potential of networking is perceived in the promotion of business by participants of the LinkedIn network, and presents two main contributions: (1) to disseminate within the business community which is the relevance given to social networking; and (2) which are the social networks best suitable to the promotion of business, to support the definition of strategies and approaches accordingly. The results confirm that LinkedIn is the most suitable network to answer the needs of those that look for professional contacts and for the promotion of business, while innovation is the most recognized factor in the promotion of business through social networking. This study contributes to a better understanding of the potential of different business social networking sites, to support organizations and professionals to align their strategies with the perceived potential of each network.

Comparative analysis between statistical and artificial intelligence models in business failure prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.

Growth and yield of corn hybrids in response to association with Azospirillum brasilense and nitrogen fertilization

There is a growing interest in optimizing the positive effects of the association between Azospirillum bacteria and corn crop in order to reduce the use of nitrogen fertilizers. This study aimed to evaluate the inoculation efficiency of an A. brasilense-based commercial product in association with different rates of nitrogen fertilization in two corn genotypes. The experiment was arranged in a 2 x 2 x 5 factorial randomized block design, with four replications. The treatments consisted of two corn hybrids (30F53 and CD386); with and without inoculation with a commercial product based on A. brasilense and five nitrogen rates (0, 40, 80, 120 and 160 kg ha-1). The variables plant height, basal stem diameter, leaf area, shoot dry matter, leaf nitrogen content, length and diameter of the cob, weight of 100 grains and grain yield were evaluated. Inoculation with A. brasilense provided increases of 11 and 12% in leaf area and shoot dry matter, respectively. There were differences in the response of the corn hybrids for most variables and the increase in nitrogen supply provided increments in the growth and yield of corn.

Comparative Analysis between Statistical and Artificial Intelligence Models in Business Failure Prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.