The RESET project: constructing a European tephra lattice for refined synchronisation of environmental and archaeological events during the last c. 100 ka

This paper introduces the aims and scope of the RESET project (. RESponse of humans to abrupt Environmental Transitions), a programme of research funded by the Natural Environment Research Council (UK) between 2008 and 2013; it also provides the context and rationale for papers included in a special volume of Quaternary Science Reviews that report some of the project's findings. RESET examined the chronological and correlation methods employed to establish causal links between the timing of abrupt environmental transitions (AETs) on the one hand, and of human dispersal and development on the other, with a focus on the Middle and Upper Palaeolithic periods. The period of interest is the Last Glacial cycle and the early Holocene (c. 100-8 ka), during which time a number of pronounced AETs occurred. A long-running topic of debate is the degree to which human history in Europe and the Mediterranean region during the Palaeolithic was shaped by these AETs, but this has proved difficult to assess because of poor dating control. In an attempt to move the science forward, RESET examined the potential that tephra isochrons, and in particular non-visible ash layers (cryptotephras), might offer for synchronising palaeo-records with a greater degree of finesse. New tephrostratigraphical data generated by the project augment previously-established tephra frameworks for the region, and underpin a more evolved tephra 'lattice' that links palaeo-records between Greenland, the European mainland, sub-marine sequences in the Mediterranean and North Africa. The paper also outlines the significance of other contributions to this special volume: collectively, these illustrate how the lattice was constructed, how it links with cognate tephra research in Europe and elsewhere, and how the evidence of tephra isochrons is beginning to challenge long-held views about the impacts of environmental change on humans during the Palaeolithic. © 2015 Elsevier Ltd.RESET was funded through Consortium Grants awarded by the Natural Environment Research Council, UK, to a collaborating team drawn from four institutions: Royal Holloway University of London (grant reference NE/E015905/1), the Natural History Museum, London (NE/E015913/1), Oxford University (NE/E015670/1) and the University of Southampton, including the National Oceanography Centre (NE/01531X/1). The authors also wish to record their deep gratitude to four members of the scientific community who formed a consultative advisory panel during the lifetime of the RESET project: Professor Barbara Wohlfarth (Stockholm University), Professor Jørgen Peder Steffensen (Niels Bohr Institute, Copenhagen), Dr. Martin Street (Romisch-Germanisches Zentralmuseum, Neuwied) and Professor Clive Oppenheimer (Cambridge University). They provided excellent advice at key stages of the work, which we greatly valued. We also thank Jenny Kynaston (Geography Department, Royal Holloway) for construction of several of the figures in this paper, and Debbie Barrett (Elsevier) and Colin Murray Wallace (Editor-in-Chief, QSR) for their considerable assistance in the production of this special volume.Peer Reviewe

European candidaemia is characterised by notable differential epidemiology and susceptibility pattern: Results from the ECMM Candida III study.

The objectives of this study were to assess Candida spp. distribution and antifungal resistance of candidaemia across Europe. Isolates were collected as part of the third ECMM Candida European multicentre observational study, conducted from 01 to 07-07-2018 to 31-03-2022. Each centre (maximum number/country determined by population size) included ∼10 consecutive cases. Isolates were referred to central laboratories and identified by morphology and MALDI-TOF, supplemented by ITS-sequencing when needed. EUCAST MICs were determined for five antifungals. fks sequencing was performed for echinocandin resistant isolates. The 399 isolates from 41 centres in 17 countries included C. albicans (47.1%), C. glabrata (22.3%), C. parapsilosis (15.0%), C. tropicalis (6.3%), C. dubliniensis and C. krusei (2.3% each) and other species (4.8%). Austria had the highest C. albicans proportion (77%), Czech Republic, France and UK the highest C. glabrata proportions (25-33%) while Italy and Turkey had the highest C. parapsilosis proportions (24-26%). All isolates were amphotericin B susceptible. Fluconazole resistance was found in 4% C. tropicalis, 12% C. glabrata (from six countries across Europe), 17% C. parapsilosis (from Greece, Italy, and Turkey) and 20% other Candida spp. Four isolates were anidulafungin and micafungin resistant/non-wild-type and five resistant to micafungin only. Three/3 and 2/5 of these were sequenced and harboured fks-alterations including a novel L657W in C. parapsilosis. The epidemiology varied among centres and countries. Acquired echinocandin resistance was rare but included differential susceptibility to anidulafungin and micafungin, and resistant C. parapsilosis. Fluconazole and voriconazole cross-resistance was common in C. glabrata and C. parapsilosis but with different geographical prevalence

Recurrent type-1 fuzzy functions approach for time series forecasting

Tak, Nihat/0000-0001-8796-5101; Egrioglu, Erol/0000-0003-4301-4149WOS: 000419006000005Forecasting the future values of a time series is a common research topic and is studied using probabilistic and non-probabilistic methods. For probabilistic methods, the autoregressive integrated moving average and exponential smoothing methods are commonly used, whereas for non-probabilistic methods, artificial neural networks and fuzzy inference systems (FIS) are commonly used. There are numerous FIS methods. While most of these methods are rule-based, there are a few methods that do not require rules, such as the type-1 fuzzy function (T1FF) approach. While it is possible to encounter a method such as an autoregressive (AR) model integrated with a T1FF, no method that includes T1FF and the moving average (MA) model in one algorithm has yet been proposed. The aim of this study is to improve forecasting by taking the disturbance terms into account. The input dataset is organized using the following variables. First, the lagged values of the time series are used for the AR model. Second, a fuzzy c-means clustering algorithm is used to cluster the inputs. Third, for the MA, the residuals of fuzzy functions are used. Hence, AR, MA, and the degree of memberships of the objects are included in the input dataset. Because the objective function is not derivative, particle swarm optimization is preferable for solving it. The results on several datasets show that the proposed method outperforms most of the methods in literature