Endocardite infectieuse: mise au point [Infective endocarditis: update]

The pathogenesis of infective endocarditis (IE) is being dissected at the molecular level, which should help redefine new preventive and therapeutic strategies against IE. In spite of improving health care, the incidence of IE has not decreased over the last decades. While classical predisposing conditions such as rheumatic heart disease were being eradicated, new features of IE have emerged. These include IE in intravenous drug users, IE in elderly patients with sclerotic valve disease, prosthetic valve IE and nosocomial IE. The epidemiology, pathogenesis, diagnosis, prevention and treatment of IE are being reviewed in this article.

Effects of sample size on the performance of species distribution models

A wide range of modelling algorithms is used by ecologists, conservation practitioners, and others to predict species ranges from point locality data. Unfortunately, the amount of data available is limited for many taxa and regions, making it essential to quantify the sensitivity of these algorithms to sample size. This is the first study to address this need by rigorously evaluating a broad suite of algorithms with independent presence-absence data from multiple species and regions. We evaluated predictions from 12 algorithms for 46 species (from six different regions of the world) at three sample sizes (100, 30, and 10 records). We used data from natural history collections to run the models, and evaluated the quality of model predictions with area under the receiver operating characteristic curve (AUC). With decreasing sample size, model accuracy decreased and variability increased across species and between models. Novel modelling methods that incorporate both interactions between predictor variables and complex response shapes (i.e. GBM, MARS-INT, BRUTO) performed better than most methods at large sample sizes but not at the smallest sample sizes. Other algorithms were much less sensitive to sample size, including an algorithm based on maximum entropy (MAXENT) that had among the best predictive power across all sample sizes. Relative to other algorithms, a distance metric algorithm (DOMAIN) and a genetic algorithm (OM-GARP) had intermediate performance at the largest sample size and among the best performance at the lowest sample size. No algorithm predicted consistently well with small sample size (n < 30) and this should encourage highly conservative use of predictions based on small sample size and restrict their use to exploratory modelling.

Vendor Managed Inventory: the Critical Success Factors from the Manufacturer Point of View

Yritykset ovat pakotettuja erilaisiin yhteistyömuotoihin pärjätäkseen kiristyvässä kilpailussa. Yhteistyösuhteet kulkevat eri nimillä riippuen teollisuuden alasta ja siitä, missä kohtaa toimitusketjua ne toteutuvat, mutta periaatteessa kaikki pohjautuvat samaan ideaan kuin Vendor Managed Inventory (VMI); varastoon jakysyntään liittyvä tieto jaetaan toimitusketjun eri osapuolien kesken, jotta tuotanto, jakelu ja varastonhallinta olisi mahdollista optimoida. Vendor Managed Inventory on ideana yksinkertainen, mutta vaatii onnistuakseen paljon. Perusolettamus on, että toimittajan on kyettävä hallinnoimaan asiakkaan varastoa paremmin kuin asiakas itse. Tämä ei kuitenkaan ole mahdollista ilman riittävää yhteistyötä, oikeanlaista informaatiota tai sopivia tuoteominaisuuksia. Tämän työn tarkoitus on esitellä kriittiset menestystekijät valmistajan kannalta, kun näkyvyys todelliseen kysyntään on heikko ja kyseessäolevat tuotteet ovat ominaisuuksiltaan toimintamalliin huonosti soveltuvia. VMItoimintamallin soveltuvuus matkapuhelimia valmistavan yrityksen liiketoimintaan, sekä sen vaikutus asiakasyhteistyöhön, kannattavuuteen ja toiminnan tehostamiseen on myös tutkittu.