Regional integration and specialisation patterns in Spain

The aim of this paper is to analyse how economic integration in Europe has affected industrial geographical concentration in Spain and explain what the driving forces behind industry location are. Firstly, we construct regional specialisation and geographical concentration indices for Spanish 50 provinces and 30 industrial sectors in 1979, 1986 and 1992. Secondly, we carry out an econometric analysis of the determinants of geographical concentration of industries. Our main conclusion is that there is no evidence of increasing specialisation in Spain between 1979 and 1992 and that the most important determinant of Spain¿s economic geography is scale economies. Furthermore, traditional trade theory has no effects in explaining the pattern of industrial concentration

To select or to weigh: a comparative study of linear combination schemes for superparent-one-dependence estimators

We conduct a large-scale comparative study on linearly combining superparent-one-dependence estimators (SPODEs), a popular family of seminaive Bayesian classifiers. Altogether, 16 model selection and weighing schemes, 58 benchmark data sets, and various statistical tests are employed. This paper's main contributions are threefold. First, it formally presents each scheme's definition, rationale, and time complexity and hence can serve as a comprehensive reference for researchers interested in ensemble learning. Second, it offers bias-variance analysis for each scheme's classification error performance. Third, it identifies effective schemes that meet various needs in practice. This leads to accurate and fast classification algorithms which have an immediate and significant impact on real-world applications. Another important feature of our study is using a variety of statistical tests to evaluate multiple learning methods across multiple data sets.

Extrapolation theory for the real interpolation method

We develop an abstract extrapolation theory for the real interpolation method that covers and improves the most recent versions of the celebrated theorems of Yano and Zygmund. As a consequence of our method, we give new endpoint estimates of the embedding Sobolev theorem for an arbitrary domain Omega

Cerebral infarction in diabetes: Clinical pattern, stroke subtypes,and predictors of in-hospital mortality

Background: To compare the characteristics and prognostic features of ischemic stroke in patients with diabetes and without diabetes, and to determine the independent predictors of in-hospital mortality in people with diabetes and ischemic stroke.Methods: Diabetes was diagnosed in 393 (21.3%) of 1,840 consecutive patients with cerebral infarction included in a prospective stroke registry over a 12-year period. Demographic characteristics, cardiovascular risk factors, clinical events, stroke subtypes, neuroimaging data, and outcome in ischemic stroke patients with and without diabetes were compared. Predictors of in-hospital mortality in diabetic patients with ischemic stroke were assessed by multivariate analysis. Results: People with diabetes compared to people without diabetes presented more frequently atherothrombotic stroke (41.2% vs 27%) and lacunar infarction (35.1% vs 23.9%) (P < 0.01). The in-hospital mortality in ischemic stroke patients with diabetes was 12.5% and 14.6% in those without (P = NS). Ischemic heart disease, hyperlipidemia, subacute onset, 85 years old or more, atherothrombotic and lacunar infarcts, and thalamic topography were independently associated with ischemic stroke in patients with diabetes, whereas predictors of in-hospital mortality included the patient's age, decreased consciousness, chronic nephropathy, congestive heart failure and atrial fibrillation. Conclusion: Ischemic stroke in people with diabetes showed a different clinical pattern from those without diabetes, with atherothrombotic stroke and lacunar infarcts being more frequent. Clinical factors indicative of the severity of ischemic stroke available at onset have a predominant influence upon in-hospital mortality and may help clinicians to assess prognosis more accurately.

Serum glycodelin pattern during the menstrual cycle in healthy young women.

OBJECTIVE: Glycodelin (PP14) is produced by the epithelium of the endometrium and its determination in the serum is used for functional evaluation of this tissue. Given the complex regulation and the combined contraceptive and immunosuppressive roles of glycodelin, the current lack of normal values for its serum concentration in the physiological menstrual cycle, derived from a large sample number, is a problem. We have therefore established reference values from over 600 sera. DESIGN: Retrospective study using banked serum samples. SETTING: University hospital. METHODS: Measurement of blood samples daily or every second day during one full cycle. MAIN OUTCOME MEASURES: Serum concentrations of glycodelin and normal values for every such one- or two-day interval were calculated. Late luteal phase glycodelin levels were compared with ovarian hormones. Follicular phase levels were compared with stimulated cycles from patients undergoing in vitro fertilization. RESULTS: Glycodelin concentrations were low around ovulation. Highest levels were observed at the end of the luteal phase; the glycodelin serum peak was reached 6-8 days after the one for progesterone. Late luteal glycodelin levels correlated negatively with the body mass index and positively with the progesterone level earlier in the secretory (mid-luteal) phase in the same woman. No associations with other ovarian hormones were observed. Follicular phase glycodelin levels were higher in the spontaneous than in the in vitro fertilization cycles. CONCLUSIONS: Normal values taken at two- or one-day intervals demonstrate the very late appearance of high serum glycodelin levels during the physiological menstrual cycle and their correlation with progesterone occurring earlier in the cycle.

Male-pattern baldness susceptibility locus at 20p11.

We conducted a genome-wide association study for androgenic alopecia in 1,125 men and identified a newly associated locus at chromosome 20p11.22, confirmed in three independent cohorts (n = 1,650; OR = 1.60, P = 1.1 x 10(-14) for rs1160312). The one man in seven who harbors risk alleles at both 20p11.22 and AR (encoding the androgen receptor) has a sevenfold-increased odds of androgenic alopecia (OR = 7.12, P = 3.7 x 10(-15)).