The yeast Cyc8 and Tup1 proteins form a corepressor complex that, when tethered to DNA, turns off transcription. Release of the Cyc8-Tup1 corepressor from a promoter has been considered as a prerequisite for subsequent transcriptional activation. Contrasting this, we demonstrate that Cyc8-Tup1 is continuously associated with target promoters under both repressive and inducing conditions. At the GAL1 promoter, Cyc8-Tup1 facilitates recruitment of SAGA (Spt-Ada-Gcn5-acetyltranferase) via Cti6, a PHD domain protein that physically links the Cyc8-Tup1 and SAGA complexes. Lack of functional corepressor renders GAL1 transcription largely independent of specific SAGA subunits. Thus, corepressor's release is not the mechanism of derepression; instead, it is the coactivator complex that alleviates Cyc8-Tup1-mediated repression under induction conditions.

Food production and energy consumption are two important factors when assessing greenhouse systems. The first must respond, both quantitatively and qualitatively, to the needs of the population, whereas the latter must be kept as low as possible. As a result, to properly control these two essential aspects, the appropriate greenhouse environment should be maintained using a computational decision support system (DSS), which will be especially adaptable to changes in the characteristics of the external environment. A multilayer perceptron neural network (MLP-NN) was designed to model the internal temperature and relative humidity of an agricultural greenhouse. The specific NN uses Levenberg–Marquardt backpropagation as a training algorithm; the input variables are the external temperature and relative humidity, wind speed, and solar irradiance, as well as the internal temperature and relative humidity, up to three timesteps before the modeled timestep. The maximum errors of the modeled temperature and relative humidity are 0.877 K and 2.838%, respectively, whereas the coefficients of determination are 0.999 for both parameters. A model with a low maximum error in predictions will enable a DSS to provide the appropriate commands to the greenhouse actuators to maintain the internal conditions at the desired levels for cultivation with the minimum possible energy consumption.

In recent years, due to the circular economy, the use of green energy forms, such as biofuels and biogas from anaerobic digestion of fermentable materials (e.g., agricultural and livestock residues) has entered our lives. According to the International Energy Agency it is estimated that the needs in 2040 will be 48% higher than in 2012 so all political decisions have converged on an urgent need for the use of more and more renewable and green energy. Considering the overall economic activity of these sectors in the region of Thessaly, the aim of this study is to highlight the residues from agricultural and livestock activities in the primary sector and calculate the annual biomass production, the methane and biogas potential, the electrical and thermal energy that can be produced from these wastes, as well as the solid residue that can be used to improve the soil of the region. The study was based on data referring to the years 2015 to 2020. The production of livestock and agricultural residues, averaged over the above six-year period in the study area, was estimated at approximately 4.8 × 106 t·yr.−1, with livestock residues accounting for 83% and agricultural residues for 17%. Furthermore, the total residues can produce an average biogas potential of approximately 4.7 × 106 m3·yr.−1, while the amount of electricity that can be produced ranges from 708–1091 GWh·yr.−1, and the corresponding thermal energy from 1112–1577 GWh·yr.−1. As a result of the complete anaerobic digestion process, a solid residue could also be obtained for the improvement of the region’s soil, which translates into a quantity in the range of 4.01 × 104 to 5.10 × 104 t·yr.−1.

The integration of photovoltaic modules into greenhouse roofs is a novel and intriguing method. Harnessing solar radiation is key to ensuring optimal crop growth, as photosynthesis relies on it. Furthermore, capturing solar radiation by employing photovoltaic systems allows energy production. Given its substantial significance in both energy generation and agriculture, this emphasizes the crucial function that solar radiation plays in these two industries. Greenhouses offer a unique opportunity to optimize both plant growth and energy generation, thereby increasing their overall worth. This approach is especially beneficial considering the growing need for land and the accompanying spatial and economic complexities. The installation of photovoltaics on the greenhouse roof has a significant impact on shading, which can be advantageous or disadvantageous, depending on the season, the crop, and the growth stage. As a result, estimating the shading in the greenhouse is imperative. In this paper, an algorithm for precisely measuring the shadowed surface area generated by solar panels within a greenhouse was developed and presented. This method also reliably determines the percentage of coverage on the whole greenhouse unit throughout the year using a time step of 10 minutes. For greenhouse operators wishing to optimize the potential of their solar panel installations, this streamlined solution provides clear and persuasive statistics.

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