Chemical properties of 11 date cultivars and their corresponding fiber extracts

Date palm fruit from 11 Tunisian cultivars (Phoenix dactylifera L.) were analyzed for their main chemical composition. Results showed that date fruits were rich in sugar (79.93 - 88.02 g/100 g dry matter), fiber(8.09 - 20.25 g/100 g dry matter) and ash (1.73 - 2.59 g/100 g dry matter). Mineral fraction was dominated by potassium and sugar fraction was dominated by reducing sugar (glucose, fructose) except for Deglet Nour, Kentichi and Bajo which are rich in sucrose. Date fiber concentrates (DFC) were extracted and analyzed for their proximate content (moisture, fiber, protein, lipid and ash) and some functional properties such as water holding capacity (WHC) and oil holding capacity (OHC). DFC presented high dietary fiber content (90.71 - 93.92 g/100g dry matter). Protein and lipid contents (dry matter basis) ranged between 3.66 and 6.06 g/100 g and between 0.35 and 1.08 g/100 g, respectively. DFC presentedhigh WHC (6.20 g water/g dry fiber) and high OHC (1.80 g oil/g dry fiber). Results showed that dates could be a valuable source of highly techno-functional fibers that could be used in food formulations

Wage led aggregate demand in the United Kingdom

The wage led aggregate demand hypothesis is examined for the United Kingdom over the period 1971 - 2007. Existing studies disagree on the aggregate demand regime for the UK, and this appears to be due to differing empirical approaches. Studies relying on equation-by-equation estimation procedures tend to find support for wage led aggregate demand in the UK, while the single study using systems estimation finds no support for the hypothesis. In order to resolve this incongruity, we test the wage led aggregate demand hypothesis in the UK using VAR models estimated on quarterly data. We use a liberal partial identification strategy based on movements in real earnings rather than in the labour share. The results provide support for the wage led aggregate demand hypothesis during the period of study

Identifying Patients With Hypoglycemia Using Natural Language Processing: Systematic Literature Review.

BACKGROUND: Accurately identifying patients with hypoglycemia is key to preventing adverse events and mortality. Natural language processing (NLP), a form of artificial intelligence, uses computational algorithms to extract information from text data. NLP is a scalable, efficient, and quick method to extract hypoglycemia-related information when using electronic health record data sources from a large population. OBJECTIVE: The objective of this systematic review was to synthesize the literature on the application of NLP to extract hypoglycemia from electronic health record clinical notes. METHODS: Literature searches were conducted electronically in PubMed, Web of Science Core Collection, CINAHL (EBSCO), PsycINFO (Ovid), IEEE Xplore, Google Scholar, and ACL Anthology. Keywords included hypoglycemia, low blood glucose, NLP, and machine learning. Inclusion criteria included studies that applied NLP to identify hypoglycemia, reported the outcomes related to hypoglycemia, and were published in English as full papers. RESULTS: This review (n=8 studies) revealed heterogeneity of the reported results related to hypoglycemia. Of the 8 included studies, 4 (50%) reported that the prevalence rate of any level of hypoglycemia was 3.4% to 46.2%. The use of NLP to analyze clinical notes improved the capture of undocumented or missed hypoglycemic events using International Classification of Diseases, Ninth Revision (ICD-9), and International Classification of Diseases, Tenth Revision (ICD-10), and laboratory testing. The combination of NLP and ICD-9 or ICD-10 codes significantly increased the identification of hypoglycemic events compared with individual methods; for example, the prevalence rates of hypoglycemia were 12.4% for International Classification of Diseases codes, 25.1% for an NLP algorithm, and 32.2% for combined algorithms. All the reviewed studies applied rule-based NLP algorithms to identify hypoglycemia. CONCLUSIONS: The findings provided evidence that the application of NLP to analyze clinical notes improved the capture of hypoglycemic events, particularly when combined with the ICD-9 or ICD-10 codes and laboratory testing

High-Normal Albuminuria and Risk of Heart Failure in the Community

Albuminuria has been associated with cardiovascular risk, but the relationship of high-normal albuminuria to subsequent heart failure has not been well established

Productive Structure in the Neo-Kaleckian Model of Growth and Distribution: Simulations to the Brazilian Economy

This chapter has as its main objective to analyze the relationship between structural change, exchange rate devaluation, growth and income distribution in Brazil. The neo-Kaleckian model of growth and distribution as designed by Cimoli et al. (2016) is simulated to the short run, where there are no restrictions to deficits on the balance of payments. This is a post-Keynesian model with Schumpeterian and Latin American structuralist ideas. The shocks in the model are made in order to understand impacts of structural change, wage level increases, and exchange rate devaluations in the Brazilian economy. To accomplish these objectives, the model is calibrated in this chapter using real data for 2011. The results indicate the dynamics of the neo-Kaleckian model and lead to an analysis of possible impacts of changes in the productive sector on growth and income distribution in Brazil

Clinical Implications of Referral Bias in the Diagnostic Performance of Exercise Testing for Coronary Artery Disease

BackgroundExercise testing with echocardiography or myocardial perfusion imaging is widely used to risk‐stratify patients with suspected coronary artery disease. However, reports of diagnostic performance rarely adjust for referral bias, and this practice may adversely influence patient care. Therefore, we evaluated the potential impact of referral bias on diagnostic effectiveness and clinical decision‐making.Methods and ResultsSearching PubMed and EMBASE (1990–2012), 2 investigators independently evaluated eligibility and abstracted data on study characteristics and referral patterns. Diagnostic performance reported in 4 previously published meta‐analyses of exercise echocardiography and myocardial perfusion imaging was adjusted using pooled referral rates and Bayesian methods. Twenty‐one studies reported referral patterns in 49 006 patients (mean age 60.7 years, 39.6% women, and 0.8% prior history of myocardial infarction). Catheterization referral rates after normal and abnormal exercise tests were 4.0% (95% CI, 2.9% to 5.0%) and 42.5% (36.2% to 48.9%), respectively, with odds ratio for referral after an abnormal test of 14.6 (10.7 to 19.9). After adjustment for referral, exercise echocardiography sensitivity fell from 84% (80% to 89%) to 34% (27% to 41%), and specificity rose from 77% (69% to 86%) to 99% (99% to 100%). Similarly, exercise myocardial perfusion imaging sensitivity fell from 85% (81% to 88%) to 38% (31% to 44%), and specificity rose from 69% (61% to 78%) to 99% (99% to 100%). Summary receiver operating curve analysis demonstrated only modest changes in overall discriminatory power but adjusting for referral increased positive‐predictive value and reduced negative‐predictive value.ConclusionsExercise echocardiography and myocardial perfusion imaging are considerably less sensitive and more specific for coronary artery disease after adjustment for referral. Given these findings, future work should assess the comparative ability of these and other tests to rule‐in versus rule‐out coronary artery disease

Airflow Obstruction, Lung Function, and Incidence of Atrial Fibrillation: The Atherosclerosis Risk in Communities (ARIC) Study

Reduced low forced expiratory volume in 1 second (FEV1) is reportedly associated with an increased risk of atrial fibrillation (AF). Extant reports do not provide separate estimates for never smokers, and for African Americans, who incongruously have lower AF incidence than Caucasians