A Method to Discriminate Between the Candida stellata and Saccharomyces cerevisiae in Mixed Fermentation on WLD and Lysine Agar Media

This paper presents a simple method to distinguish between Candida stellata and Saccharomyces cerevisiaeyeasts during microbiological analyses. The method is based on differential yeast growth on a mediumcontaining cycloheximide and a medium containing lysine as only nitrogen source (lysine agar). Thecycloheximide resistance of 45 yeast strains belonging to Candida stellata, Hanseniaspora uvarum, Hanseniasporaguilliermondii, Metschnikowia pulcherrima, Torulaspora delbrueckii, Zygosaccharomyces bailii, Kluyveromycesthermotolerans and Zygoascus hellenicus, and 14 strains of Saccharomyces cerevisiae and Saccharomycesbayanus on WL nutrient agar, was assayed. Cycloheximide resistance is characteristic of the species H. uvarum,H. guilliermondii and Z. hellenicus, while for the other yeasts it depends on the strain and the concentrationof cycloheximide used. Two mg/L of cycloheximide allows selective counting of a strain of C. stellata (Cs3)compared to one of the sensitive S. cerevisiae strain (NDA21). Similar results can be obtained on lysine agar,but counts are reliable only with the additional spreading of a monolayer of Saccharomyces cells. The differentcycloheximide resistance of C. stellata and S. cerevisiae can be used in the microbiological analysis of mixedcultures to monitor the individual growth of the two yeast species. This method can be applied to the studyof mixed fermentations with other non-Saccharomyces species. The modified use of lysine agar is useful to acertain extent in the distinction of multistarter yeasts from the indigenous yeasts

Macromodeling strategy for digital devices and interconnects

International audienceThis paper proposes a macromodeling approach for the simulation of digital interconnected systems. Such an approach is based on a set of macromodels describing IC ports, IC packages and multiconductor interconnect structures in standard circuit simulators, like SPICE. We illustrate the features of the macromodels and we demonstrate the proposed approach on a realistic simulation problem

Knowledge Rich Natural Language Queries over Structured Biological Databases

Increasingly, keyword, natural language and NoSQL queries are being used for information retrieval from traditional as well as non-traditional databases such as web, document, image, GIS, legal, and health databases. While their popularity are undeniable for obvious reasons, their engineering is far from simple. In most part, semantics and intent preserving mapping of a well understood natural language query expressed over a structured database schema to a structured query language is still a difficult task, and research to tame the complexity is intense. In this paper, we propose a multi-level knowledge-based middleware to facilitate such mappings that separate the conceptual level from the physical level. We augment these multi-level abstractions with a concept reasoner and a query strategy engine to dynamically link arbitrary natural language querying to well defined structured queries. We demonstrate the feasibility of our approach by presenting a Datalog based prototype system, called BioSmart, that can compute responses to arbitrary natural language queries over arbitrary databases once a syntactic classification of the natural language query is made

The Overlooked Potential of Generalized Linear Models in Astronomy - I: Binomial Regression

Revealing hidden patterns in astronomical data is often the path to fundamental scientific breakthroughs; meanwhile the complexity of scientific inquiry increases as more subtle relationships are sought. Contemporary data analysis problems often elude the capabilities of classical statistical techniques, suggesting the use of cutting edge statistical methods. In this light, astronomers have overlooked a whole family of statistical techniques for exploratory data analysis and robust regression, the so-called Generalized Linear Models (GLMs). In this paper -- the first in a series aimed at illustrating the power of these methods in astronomical applications -- we elucidate the potential of a particular class of GLMs for handling binary/binomial data, the so-called logit and probit regression techniques, from both a maximum likelihood and a Bayesian perspective. As a case in point, we present the use of these GLMs to explore the conditions of star formation activity and metal enrichment in primordial minihaloes from cosmological hydro-simulations including detailed chemistry, gas physics, and stellar feedback. We predict that for a dark mini-halo with metallicity $\approx 1.3 \times 10^{-4} Z_{\bigodot}$, an increase of $1.2 \times 10^{-2}$ in the gas molecular fraction, increases the probability of star formation occurrence by a factor of 75%. Finally, we highlight the use of receiver operating characteristic curves as a diagnostic for binary classifiers, and ultimately we use these to demonstrate the competitive predictive performance of GLMs against the popular technique of artificial neural networks.Comment: 20 pages, 10 figures, 3 tables, accepted for publication in Astronomy and Computin

Finite driving rate and anisotropy effects in landslide modeling

In order to characterize landslide frequency-size distributions and individuate hazard scenarios and their possible precursors, we investigate a cellular automaton where the effects of a finite driving rate and the anisotropy are taken into account. The model is able to reproduce observed features of landslide events, such as power-law distributions, as experimentally reported. We analyze the key role of the driving rate and show that, as it is increased, a crossover from power-law to non power-law behaviors occurs. Finally, a systematic investigation of the model on varying its anisotropy factors is performed and the full diagram of its dynamical behaviors is presented.Comment: 8 pages, 9 figure

Presence of Candida zemplinina in Sicilian Musts and Selection of a Strain for Wine Mixed Fermentations

The purpose of this work was to investigate the presence of C. zemplinina yeasts in Sicilian musts andgrapes and to identify strains of oenological interest. We report on the taxonomical reclassificationof Candida yeast isolates from Sicilian musts and on the selection of one strain of oenological interest(Cz3), based on mixed micro-fermentation experiments in sterile Nero d’Avola musts. Our results showthat Candida zemplinina is abundant in Sicilian grapes and musts, and that the Cz3 strain is suitablefor Candida zemplinina/Saccharomyces cerevisiae mixed fermentations. The higher glycerol content andthe lower ethanol level stood out as the most promising features of the wines obtained upon sequentialinoculation of the Cz3 and (S. cerevisiae) NDA21 yeast starters. We therefore have isolated a Sicilian Czstrain endowed with very promising features for the future development of mixed fermentation protocols

From Sensing to Action: Quick and Reliable Access to Information in Cities Vulnerable to Heavy Rain

Cities need to constantly monitor weather to anticipate heavy storm events and reduce the impact of floods. Information describing precipitation and ground conditions at high spatio-temporal resolution is essential for taking timely action and preventing damages. Traditionally, rain gauges and weather radars are used to monitor rain events, but these sources provide low spatial resolutions and are subject to inaccuracy. Therefore, information needs to be complemented with data from other sources: from citizens' phone calls to the authorities, to relevant online media posts, which have the potential of providing timely and valuable information on weather conditions in the city. This information is often scattered through different, static, and not-publicly available databases. This makes it impossible to use it in an aggregate, standard way, and therefore hampers efficiency of emergency response. In this paper, we describe information sources relating to a heavy rain event in Rotterdam on October 12-14, 2013. Rotterdam weather monitoring infrastructure is composed of a number of rain gauges installed at different locations in the city, as well as a weather radar network. This sensing network is currently scarcely integrated and logged data are not easily accessible during an emergency. Therefore, we propose a reliable, efficient, and low-cost ICT infrastructure that takes information from all relevant sources, including sensors as well as social and user contributed information and integrates them into a unique, cloud-based interface. The proposed infrastructure will improve efficiency in emergency responses to extreme weather events and, ultimately, guarantee more safety to the urban population