Deepfield connect, an innovative decision support system for crops irrigation management under Mediterranean conditions

The irrigation management, in the Mediterranean region, represents an important technique useful to reach sustainable yield and improve the quality of the crop. The use of decision support systems and water saving techniques has gained importance during the last decades mainly in arid and semiarid countries where water is considered a precious resource. DeepField Connect by BOSCH is an innovative tool able to support farmers in irrigation management and consists of three main parts: hardware (sensors, device-to-web-data logger and thermo-hygrometer), algorithm and graphic use interface (app). This system is based on GIS analysis, which represents the most innovative and functional tool for such studies, which provides a mapping of soil hydrological characteristics at the regional level. We used, as a reference, soil data analysis obtained at Regional level from the ACLA II Project. In this way, the system creates an interactive mapping system, matching each point of the Apulian surface, in particular, the texture composition of the soil and the values of the hydrological constants (wilting point, WP and field capacity FC), for irrigation planning. These data are integrated with the recharging point (RP) a value calculated for the main regional irrigated crop which represents the level of soil moisture that, together with FC, represent the range of plant-available water. Besides, this tool provides different irrigation strategies such as deficit irrigation or complete restitution of evapotranspiration losses, according to farmer needs. DeepField Connect by BOSCH transmits the data via the Bosch Cloud to the smartphone. This allows to keep track of fields at any given time and to provide assistance in: when to irrigate and which irrigation volumes to use. This intelligent system can be considered as the application of one of the best practices that the agricultural sector can implement to improve its environmental performance and contribute to sustainable food production

Representative transcript sets for evaluating a translational initiation sites predictor

<p>Abstract</p> <p>Background</p> <p>Translational initiation site (TIS) prediction is a very important and actively studied topic in bioinformatics. In order to complete a comparative analysis, it is desirable to have several benchmark data sets which can be used to test the effectiveness of different algorithms. An ideal benchmark data set should be reliable, representative and readily available. Preferably, proteins encoded by members of the data set should also be representative of the protein population actually expressed in cellular specimens.</p> <p>Results</p> <p>In this paper, we report a general algorithm for constructing a reliable sequence collection that only includes mRNA sequences whose corresponding protein products present an average profile of the general protein population of a given organism, with respect to three major structural parameters. Four representative transcript collections, each derived from a model organism, have been obtained following the algorithm we propose. Evaluation of these data sets shows that they are reasonable representations of the spectrum of proteins obtained from cellular proteomic studies. Six state-of-the-art predictors have been used to test the usefulness of the construction algorithm that we proposed. Comparative study which reports the predictors' performance on our data set as well as three other existing benchmark collections has demonstrated the actual merits of our data sets as benchmark testing collections.</p> <p>Conclusion</p> <p>The proposed data set construction algorithm has demonstrated its property of being a general and widely applicable scheme. Our comparison with published proteomic studies has shown that the expression of our data set of transcripts generates a polypeptide population that is representative of that obtained from evaluation of biological specimens. Our data set thus represents "real world" transcripts that will allow more accurate evaluation of algorithms dedicated to identification of TISs, as well as other translational regulatory motifs within mRNA sequences. The algorithm proposed by us aims at compiling a redundancy-free data set by removing redundant copies of homologous proteins. The existence of such data sets may be useful for conducting statistical analyses of protein sequence-structure relations. At the current stage, our approach's focus is to obtain an "average" protein data set for any particular organism without posing much selection bias. However, with the three major protein structural parameters deeply integrated into the scheme, it would be a trivial task to extend the current method for obtaining a more selective protein data set, which may facilitate the study of some particular protein structure.</p

Risk factors for endometrial cancer in black and white women: a pooled analysis from the epidemiology of endometrial cancer consortium (E2C2)

Endometrial cancer (EC) is the most common gynecologic cancer in the United States. Over the last decade, the incidence rate has been increasing, with a larger increase among blacks. The aim of this study was to compare risk factors for EC in black and white women

Inhibitory Activity of Illicium verum Extracts against Avian Viruses

This study aimed at screening the inhibitory activity of Illicium verum extracts against avian reovirus, infectious bursal disease virus (IBDV), Newcastle disease virus (NDV), and infectious laryngotracheitis virus (ILTV). The cytotoxic and antiviral actions of 3 extracts, absolute methanol (100MOH), 50% methanol (50MOH), and aqueous extracts (WA.), were evaluated by MTT assay. The Illicium verum extracts were added to the cultured chick embryo fibroblast (CEF) with tested viruses in three attacks, preinoculation, postinoculation, and simultaneous inoculation. The three extracts showed antiviral inhibitory activity against all tested viruses during simultaneous inoculation and preinoculation except 100MOH and 50MOH that showed no effect against IBDV, thereby suggesting that the extracts have a preventive effect on CEF against viruses. During postinoculation, the extracts exhibited inhibitory effects against NDV and avian reovirus, while no effect against IBDV recorded and only the 100MOH showed an inhibitory effect against ILTV. The initial results of this study suggest that Illicium verum may be a candidate for a natural alternative source for antiviral agents

Different Suitability of Olive Cultivars Resistant to Xylella fastidiosa to the Super-Intensive Planting System

Until today, only Leccino and Fs-17 (=Favolosa®) olive cultivars proved resistant to Xylella fastidiosa subsp. pauca (Xfp) due to a low presence of bacteria in the xylem. Integrated disease management in olive growing areas threatened by the spread of Xfp is crucial to overcoming the environmental, economic and social crisis. Since the EU Decision allows for the plantation of resistant olive cultivars in infected areas, there is a need to define a suitable plantation system for these cultivars. The adoption of new planting systems, such as intensive and super-intensive (SHD), could compensate for the economic losses and restore the olive agroecosystem. The aim is to ascertain the suitability of the available Xfp-resistant cultivars to SHD planting systems that demonstrate the best economic and environmental sustainability. Hence, a five-year study was established in an experimental SHD olive orchard (Southern Italy) in order to analyse the main vegetative and productive traits of Leccino and Fs-17, together with four other Italian cultivars (Cipressino, Coratina, Frantoio and Urano), compared with the well-adapted cultivars to SHD orchards (Arbequina and Arbosana), by means of the von Bertalanffy function. The results indicated that cv. Fs-17 showed sufficient suitability for SHD planting systems, giving the best-accumulated yield despite some canopy growth limitations, whereas cv. Leccino did not show satisfactory results in terms of both vegetative and yield parameters, confirming its suitability for intensive planting systems. These results are useful for optimizing integrated resistance management in Xfp-infected areas by planting resistant host plants

Prominent bactericidal characteristics of silver-copper nanocomposites produced via pulse laser ablation

This paper reports the characterization and antibacterial performance evaluation of some spherical and stable crystalline silver (Ag)/copper (Cu) nanocomposites (Ag-CuNCs) prepared in deionized water (DIW) using pulse laser ablation in liquid (PLAL) method. The influence of various laser fluences (LFs) on the structural, morphological, optical and antibacterial properties of these NCs were determined. The UV–Vis absorbance of these NCs at 403 nm and 595 nm was gradually increased accompanied by a blue shift. XRD patterns disclosed the nucleation of highly crystalline Ag-CuNCs with their face centered cubic lattice structure. TEM images showed the existence of spherical NCs with size range of 3–20 nm and lattice fringe spacing of approximately 0.145 nm. EDX profiles of Ag-CuNCs indicated their high purity. The antibacterial effectiveness of the Ag-CuNCs was evaluated by the inhibition zone diameter (IZD) and optical density (OD600) tests against Gram-negative (Escherichia coli) and Gram-positive (Staphylococcus aureus) bacteria. The proposed NCs revealed the IZD values in the range of 22–26 mm and 20–25 mm when tested against E. coli and S. aureus bacteria, respectively. The Ag-CuNCs prepared at LF of 14.15 J/cm2 revealed the best bactericidal activity. It is established that by controlling the laser fluence the bactericidal effectiveness of the Ag-CuNCs can be tuned

Prediction of Stem Water Potential in Olive Orchards Using High-Resolution Planet Satellite Images and Machine Learning Techniques

Assessing plant water status accurately in both time and space is crucial for maintaining satisfactory crop yield and quality standards, especially in the face of a changing climate. Remote sensing technology offers a promising alternative to traditional in situ measurements for estimating stem water potential (Ψstem). In this study, we carried out field measurements of Ψstem in an irrigated olive orchard in southern Italy during the 2021 and 2022 seasons. Water status data were acquired at midday from 24 olive trees between June and October in both years. Reflectance data collected at the time of Ψstem measurements were utilized to calculate vegetation indices (VIs). Employing machine learning techniques, various prediction models were developed by considering VIs and spectral bands as predictors. Before the analyses, both datasets were randomly split into training and testing datasets. Our findings reveal that the random forest model outperformed other models, providing a more accurate prediction of olive water status (R2 = 0.78). This is the first study in the literature integrating remote sensing and machine learning techniques for the prediction of olive water status in order to improve olive orchard irrigation management, offering a practical solution for estimating Ψstem avoiding time-consuming and resource-intensive fieldwork