Combine use of Selected Schizosaccharomyces pombe and Lachancea thermotolerans Yeast Strains as an Alternative to the Traditional Malolactic Fermentation in Red Wine Production

Most red wines commercialized in the market use the malolactic fermentationprocess in order to ensure stability from a microbiological point of view. In this secondfermentation, malic acid is converted into L-lactic acid under controlled setups. Howeverthis process is not free from possible collateral effects that on some occasions produceoff-flavors, wine quality loss and human health problems. In warm viticulture regions suchas the south of Spain, the risk of suffering a deviation during the malolactic fermentationprocess increases due to the high must pH. This contributes to produce wines with highvolatile acidity and biogenic amine values. This manuscript develops a new red winemakingmethodology that consists of combining the use of two non-Saccharomyces yeast strains asan alternative to the traditional malolactic fermentation. In this method, malic acid is totallyconsumed by Schizosaccharomyces pombe, thus achieving the microbiological stabilizationobjective, while Lachancea thermotolerans produces lactic acid in order not to reduce andeven increase the acidity of wines produced from low acidity musts. This technique reducesthe risks inherent to the malolactic fermentation process when performed in warm regions.The result is more fruity wines that contain less acetic acid and biogenic amines than thetraditional controls that have undergone the classical malolactic fermentation

Extreme climate variability should be considered in forestry-assisted migration

International audienceRecently, Pedlar et al. (2012) stated that assisted migration in forestry (forestry AM) differs from species-rescue-assisted migration (species rescue AM) because the risks of invasiveness, hybridization with local species, and spread of diseases are minimized in managed forests. The rationale behind this assertion for forestry AM is that it involves the translocation of populations within the existing geographic range of the species, whereas species rescue AM involves the introduction of exotic species. However, while we agree that forestry AM is less risky than species rescue AM for the recipient ecosystem, forestry AM can not only fail but can also incur enormous financial costs. The failure of efforts that involved planting maritime pine (Pinus pinaster Aït) trees in Southwest France (Aquitaine) with seeds from more southerly populations from Portugal for production purposes is a textbook case. The climate variability in Aquitaine includes periods of intense frost that are sufficiently rare (every 10 to 20 years) to be overlooked when establishing tree populations. The frost of the winter of 1985, the most intense frost event since records began with temperatures dropping as low as -22 °C (Boisseaux, 1986), affecting about 350 km2 of tree plantations in the region (Doré & Varoquaux, 2006). The highest mortality related to frost was observed in populations harvested from Leiria in Portugal, for which nearby records show that the absolute minimum temperature was only -7.8 °C in the last 60 years. Climate averages over the last 30 years differ only slightly between Leiria and Aquitaine, which would erroneously suggest that samples from Portugal would have survived in the Aquitaine region. Newly emerging climates (Williams et al. 2007) and the uncertainty related to climate change extreme events (Easterling, 2000) will make the search for southern locations with climatic conditions similar to those of northern populations of trees extremely difficult. Policies of forest adaptation to climate change should account for extreme cold events in the target populations even if climate change will likely decrease the number of extreme cold events (Easterling, 2000), that remain in our opinion, the hidden element behind the maladaptation of southern populations to northern locations

Metabolic risk score indexes validation in overweight healthy people

The constellation of adverse cardiovascular disease (CVD) and metabolic risk factors, including elevated abdominal obesity, blood pressure (BP), glucose, and triglycerides (TG) and lowered high-density lipoprotein-cholesterol (HDL-C), has been termed the metabolic syndrome (MetSyn) [1]. A number of different definitions have been developed by the World Health Organization (WHO) [2], the National Cholesterol Education Program Adult Treatment Panel III (ATP III) [3], the European Group for the Study of Insulin Resistance (EGIR) [4] and, most recently, the International Diabetes Federation (IDF) [5]. Since there is no universal definition of the Metabolic Syndrome, several authors have derived different risk scores to represent the clustering of its components [6-11]

Extreme pressure behaviour of newly formulated oil-in-water emulsions

Oil-in-water (O/W) emulsions are broadly used in metal-machining processes, where combined lubrication and refrigeration are needed, such as in cutting, rolling, or grinding. These fluids consist of tiny oil droplets in water stabilised by small amounts of emulsifiers, namely surfactants. In an emulsion, oil is responsible for the lubricating properties, whereas water provides heat dissipation and fire resistance. Normally, emulsifiable metalworking oils are used in an oil concentration between 2 and 5 vol. %, depending on the application. Despite their wide use, the lubrication mechanisms of o/w emulsions have not been fully understood, mainly because of their complexity. Previous studies on oil-in-water emulsions showed that, in order to form thick lubricant films, oil droplets must wet the metal surfaces, displacing water. The ability of oil to wet is strongly dependent on the concentration of surfactant. Surfactant molecules tend to adsorb preferentially at the interface, modifying the nature of the layers adjacent to the metal surfaces and, thus, playing a key role in processes such as wettability, corrosion, or friction, as well as emulsion stability. The aim of this work is to study the influence of concentration of two different emulsifiers (anionic and non-inonic) on the wettability and extreme pressure properties of an oil-in-water emulsion. A mixture of a synthetic polyalphaolefin and a trimethylol propane ester was used as the base oil, and the concentrations of emulsifiers were below, equal to, and above their critical micellar concentrations (CMC). Extreme pressure tests (ASTM D 2783), which try to simulate the operating conditions of high speeds and pressures taking place in cutting processes, and contact angle measurements were carried out in order to establish a relationship between both properties and to evaluate the performance of these emulsions as lubricants

Neural node network and model, and method of teaching same

The present invention is a fully connected feed forward network that includes at least one hidden layer 16. The hidden layer 16 includes nodes 20 in which the output of the node is fed back to that node as an input with a unit delay produced by a delay device 24 occurring in the feedback path 22 (local feedback). Each node within each layer also receives a delayed output (crosstalk) produced by a delay unit 36 from all the other nodes within the same layer 16. The node performs a transfer function operation based on the inputs from the previous layer and the delayed outputs. The network can be implemented as analog or digital or within a general purpose processor. Two teaching methods can be used: (1) back propagation of weight calculation that includes the local feedback and the crosstalk or (2) more preferably a feed forward gradient decent which immediately follows the output computations and which also includes the local feedback and the crosstalk. Subsequent to the gradient propagation, the weights can be normalized, thereby preventing convergence to a local optimum. Education of the network can be incremental both on and off-line. An educated network is suitable for modeling and controlling dynamic nonlinear systems and time series systems and predicting the outputs as well as hidden states and parameters. The educated network can also be further educated during on-line processing

Pipeline for recording datasets and running neural networks on the Bela embedded hardware platform

Deploying deep learning models on embedded devices is an arduous task: oftentimes, there exist no platform-specific instructions, and compilation times can be considerably large due to the limited computational resources available on-device. Moreover, many music-making applications de- mand real-time inference. Embedded hardware platforms for audio, such as Bela, offer an entry point for beginners into physical audio computing; however, the need for cross- compilation environments and low-level software develop- ment tools for deploying embedded deep learning models imposes high entry barriers on non-expert users. We present a pipeline for deploying neural networks in the Bela embedded hardware platform. In our pipeline, we include a tool to record a multichannel dataset of sen- sor signals. Additionally, we provide a dockerised cross- compilation environment for faster compilation. With this pipeline, we aim to provide a template for programmers and makers to prototype and experiment with neural networks for real-time embedded musical applications