Effects of temperature, ammonium and glucose concentrations on yeast growth in a model wine system

In enology, alcoholic fermentation is a complex process involving several mechanisms. Slow and incomplete alcoholic fermentation is a chronic problem for the wine industry and factors leading to sluggish and stuck fermentations have been extensively studied and reviewed. The most studied cause of sluggish and stuck fermentation is the nitrogen content limitation. Nevertheless, other factors, such as temperature of fermentation and sugar concentration can affect the growth of yeasts. In this study we modelled the yeast growth‐cycle in wine model system as a function of temperature, sugar and ammonium concentrations; the individual effects and the interaction of these factors were analysed by means of a quadratic response surface methodology. Cell concentrations and weight loss were monitored in the whole wine fermentation process. The results of central composite design show that lower is the availability of nitrogen, higher is the cell growth rate; moreover, initial nitrogen concentration also influences survival time of Saccharomyces cerevisiae.     

Automatic recognition fingerspelling gestures in multiple languages for a communication interface for the disabled

This paper presents the design and evaluation of a multi-lingual fingerspelling recognition module that is designed for an information terminal. Through the use of multimodal input and output methods, the information terminal acts as a communication medium between deaf and blind people. The system converts fingerspelled words to speech and vice versa using fingerspelling recognition, fingerspelling synthesis, speech recognition and speech synthesis in Czech, Russian, and Turkish languages. We describe an adaptive skin color based fingersign recognition system with a close to real-time performance and present recognition results on 88 different letters signed by five different signers, using above four hours of training and test videos.     

Microfluidic motion for a direct investigation of solvent interactions with PDMS microchannels

Solid surface/liquid interactions play an important role in microfluidics and particularly in manipulation of films, drops and bubbles, a basic requirement for a number of lab-on-chip applications. The behavior of solvents in coated microchannels is difficult to be predicted considering theories; therefore, experimental methods able to estimate the properties at the interface in real time and during the operational regime are amenable. Here, we propose to use an experimental setup to evaluate the effective dynamics of solvents inside PDMS microchannels. The influence of the solvent properties as well as of the channel wall’s wettability on the fluid movements was evaluated. Modification of the channel properties was achieved by introducing Teflon coatings that allow producing stable hydrophobic microchannel walls. The results were fitted according to Washburn-type power-law and compared with theoretical calculations of the parameter β that expresses the dependence of capillary dynamics on surface tension γ, liquid viscosity η, contact angles θ and the hydraulic radius R _H. A comparison between the calculated and the experimental values reveled that parameters other than the contemplated ones influenced the measurements. The main parameter that affects the flow of solvents such as water, methanol ethanol, dimethylformamide, acetonitrile and acetone was found to be the γ/η ratio. Considering these results, the investigation tool described here is believed to be promising to predict the dynamics of common organic solvents inside integrated functional fluidic devices and to accurately control solvent flow, particularly in capillary-driven pumpless systems, a basic requirement for widening the application range of PDMS lab-on-chip devices.     

The Effects Of Humic Substances On Pinus Callus Are Reversed By 2,4-Dichlorophenoxyacetic Acid

The reversal of humic matter-induced inhibition of callus growth and metabolism by 2,4-dichlorophenoxyacetic acid (2,4-D) was studied in Pinus laricio. Two forest humic fractions (relative molecular mass (M_r) > 3500), derived from soil under Fagus sylvatica (Fs) and Abies alba (Aa) plantation, were used. Pinus laricio callus was grown for a subculture period (4 weeks) on Basal Murashige and Skoog (MS) medium plus forest humic matters (Fs or Aa), at a concentration of 1 mg C/l, and then was transferred, for an additional four weeks, to a MS medium culture without humic matter, but with different hormones: indole-3-acetic acid (IAA, 2 mg/1) or 2,4-dichlorophenoxyacetic acid (2,4-D, 0.5 mg/1) and/or 6-benzylaminopurine (BAP, 0.25 mg/1). Growth of calluse, glucose, fructose, and sucrose contents, and activities of soluble and bound invertases, glucokinase, phosphoglucose isomerase, aldolase, and pyruvate kinase were monitored. The results show a negative effect of humic fractions on callus growth, due to decreased utilization of glucose and fructose, and decreased activities of glycolytic enzymes. The effects are reversible. Substitution of humic fractions with 2,4-D+BAP or 2,4-D is followed by an increase of glycolytic enzyme activities and, consequently, by the utilization of glucose and fructose that induces a restart of growth. In contrast, the inhibitory effects of humic fractions persist when they are substituted with BAP alone, indicating that only the auxin 2,4-D is capable of reversing the negative effects. A possible competitive action on the auxin-binding site between 2,4-D and the chemical structures in the forest humic fractions is suggested.     

Spatio-temporal hotspots and application on a disease analysis case via GIS

Hotspot analysis is a spatial analysis that uses cluster techniques for determining areas with elevated concentrations of localized events. We use the consolidated Extended Fuzzy C-Means algorithm to determine the hotspot areas on the map as circles, moreover the advantages of this technique are the linear computational complexity, the robustness to noise and outliers, the automatic determination of the optimal number C of clusters (in the classical FCM algorithm C is chosen a priori). Furthermore it prevents the problem of shifting the clusters with low density area of data points in areas with higher density of such points. We apply this method to study the spatio-temporal variations of the hotspot areas by testing this process on a specific disease problem, precisely we have clusterized 5,000 point-events correspondent to cases of brain cancer detected in the state of New Mexico from 1973 to 1991. We also show that the same results are obtained by using the Extended Gustafson–Kessel algorithm which gives elliptical clusters. We have implemented both algorithms in a Geographic Information System environment. Thus we establish the areas which seem not interested from the incidence of the disease and those areas in which the phenomenon appears to be temporarily attenuated either increased or constant or quite disappeared.     

Simulating protection rackets: a case study of the Sicilian Mafia

Protection racketeering groups are powerful, deeply entrenched in multiple societies across the globe, and they harm the societies and economies in which they operate in multiple ways. These reasons make their dynamics important to understand and an objective of both scientific and application-oriented interest. Legal and social norm-based approaches arguably play significant roles in influencing protection racket dynamics. We propose an agent-based simulation model, the Palermo Scenario, to enrich our understanding of these influences and to test the effect of different policies on protection racket dynamics. Our model integrates the legal and the social norm-based approaches and uses a complex normative agent architecture that enables the analysis of both agents’ behaviours and mental normative representations driving behaviour. We demonstrate the usefulness of the model and the benefits of using this complex normative architecture through a case study of the Sicilian Mafia.     

Robust procedures in multiple regression: P-subsets and a computational proposal

A number of robust and diagnostic techniques for the linear regression are in terms of p-dimensional subsets of the original sample. In a sample of size n this leads to consider ⁿ C _p subsets. To reduce the computational burden, a limited number of subsets can be selected by means of a sub-sampling scheme. In this paper we suggest and discuss the use of a Balanced Incomplete Block design plan.