Structure and classification of zoophytes. By James D. Dana.

c. 2

Enhancing text classification to improve information filtering

Text classification, information filtering, semi-supervised learning, quality control

Partially supervised learning of fuzzy classification rules

Fuzzy classification, semi-supervised learning, data mining

Validation of the Killip-Kimball Classification and Late Mortality after Acute Myocardial Infarction

Background: The classification or index of heart failure severity in patients with acute myocardial infarction (AMI) was proposed by Killip and Kimball aiming at assessing the risk of in-hospital death and the potential benefit of specific management of care provided in Coronary Care Units (CCU) during the decade of 60. Objective: To validate the risk stratification of Killip classification in the long-term mortality and compare the prognostic value in patients with non-ST-segment elevation MI (NSTEMI) relative to patients with ST-segment elevation MI (STEMI), in the era of reperfusion and modern antithrombotic therapies. Methods: We evaluated 1906 patients with documented AMI and admitted to the CCU, from 1995 to 2011, with a mean follow-up of 05 years to assess total mortality. Kaplan-Meier (KM) curves were developed for comparison between survival distributions according to Killip class and NSTEMI versus STEMI. Cox proportional regression models were developed to determine the independent association between Killip class and mortality, with sensitivity analyses based on type of AMI. Results: The proportions of deaths and the KM survival distributions were significantly different across Killip class >1 (p <0.001) and with a similar pattern between patients with NSTEMI and STEMI. Cox models identified the Killip classification as a significant, sustained, consistent predictor and independent of relevant covariables (Wald χ2 16.5 [p = 0.001], NSTEMI) and (Wald χ2 11.9 [p = 0.008], STEMI). Conclusion: The Killip and Kimball classification performs relevant prognostic role in mortality at mean follow-up of 05 years post-AMI, with a similar pattern between NSTEMI and STEMI patients.

Prototype-based classification and clustering

Magdeburg, Univ., Fak. für Informatik, Habil.-Schr., 2006

Efeitos do enxofre e do boro e da inoculação em alfafa (Medicago sativa L.)

This paper deals with the results of a pot and plot experiment which was carried out to determine the influence of sulphur and boron and the effect seed inoculation with Rhizobium meliloti in the yield of alfafa. Sulphur was applied as flower of sulphur at the rates of 1,000 and 2,000 kg por hectare; boron was employed in the proportion of 15 kg of borax per hectare; both sulphur and boron were distributed broadcast before planting; the experimental design chosen for the field trial was a latin square of 6 x 6 with the following treatments: Number Treatment 1 Control 2 One dosis S + inoculation 3 Two dosis S +inoculation 4 One dosis S + B + inoculation 5 B + inoculation 6 inoculation The crop supplied four cuttings in an eleven months period. The pot experiment nearly confirmed the plot one. The following conclusions can be drawn: 1. The classification of treatments in a decrescent order was: l.o - two doses S + inoculation; 2.o - one dosis S +inoculation, S + B + inoculation, and B + inoculation (these treatmente were not statistically different); 3.o - control; 4.o - inoculation; 2 The vield due to the treatment two dosis S + inoculation was 22 per cent higher than the control one, a fact that suggests that the S supply in the soil studied ("terra roxa misturada") is not sufficient for the total requirements of alfafa; 3. From an economical point of view the best treatment was: one dosis B + inoculation which permits a net gain of Cr$ 12.527,30 per hectare per year; 4. Based on the mentioned results we recommend in soils of same type the following fertilization for alfafa. 5 tons limestone/hectare 300 kg serranafosfato and 600 kg hiperfosfato/ha 300 kg muriate of potash/ha 15 kg borax/ha and a medium organic manuring if the soil is very poor in organic matter.

Visual analysis, clustering, and classification of contrast-enhanced tumor perfusion MRI data

Magdeburg, Univ., Fak. für Inf., Diss., 2014