Bisphenol A Effects in Aqueous Environment on Lemna minor

The link between different plastic waste pollutants and their impact on the natural aquatic environment and food chain remains a constant and growing issue. Bisphenol A (BPA), a known endocrine disruptor produced in large quantities primarily in the industry of polycarbonate plastics, can accumulate in vegetal and animal tissue, thus magnifying through trophic levels. In this study we exposed viable specimens of the aquatic plant Lemna minor under controlled conditions to 50, 100 and 200 ppm BPA levels in order to partially observe the toxic effects of BPA. Colonies ceased to form during the exposure and chlorosis was present especially in the 100 ppm group. Interestingly enough, a high density formation of non-fermenting bacteria as well as coliforms was also observed in the BPA exposed cultures but not in the control groups. The levels of Malondialdehyde (MDA) in the vegetal tissue indicated cellular insults and severe damage, results that were correlated with the HPLC BPA determined concentrations of 0.1%, 0.2% and 0.4%.

Classification of the Microstructural Elements of the Vegetal Tissue of the Pumpkin (Cucurbita pepo L.) Using Convolutional Neural Networks

Although knowledge of the microstructure of food of vegetal origin helps us to understand the behavior of food materials, the variability in the microstructural elements complicates this analysis. In this regard, the construction of learning models that represent the actual microstructures of the tissue is important to extract relevant information and advance in the comprehension of such behavior. Consequently, the objective of this research is to compare two machine learning techniques—Convolutional Neural Networks (CNN) and Radial Basis Neural Networks (RBNN)—when used to enhance its microstructural analysis. Two main contributions can be highlighted from this research. First, a method is proposed to automatically analyze the microstructural elements of vegetal tissue; and second, a comparison was conducted to select a classifier to discriminate between tissue structures. For the comparison, a database of microstructural elements images was obtained from pumpkin (Cucurbita pepo L.) micrographs. Two classifiers were implemented using CNN and RBNN, and statistical performance metrics were computed using a 5-fold cross-validation scheme. This process was repeated one hundred times with a random selection of images in each repetition. The comparison showed that the classifiers based on CNN produced a better fit, obtaining F1–score average of 89.42% in front of 83.83% for RBNN. In this study, the performance of classifiers based on CNN was significantly higher compared to those based on RBNN in the discrimination of microstructural elements of vegetable foods.

Real-Time Impedance Monitoring During Electroporation Processes in Vegetal Tissue Using a High-Performance Generator

Classical application of electroporation is carried out by using fixed protocols that do not clearly assure the complete ablation of the desired tissue. Nowadays, new methods that pursue the control of the treatment by studying the change in impedance during the applied pulses as a function of the electric field are being developed. These types of control seek to carry out the treatment in the fastest way, decreasing undesired effects and treatment time while ensuring the proper tumour ablation. The objective of this research is to determine the state of the treatment by continuously monitoring the impedance by using a novel versatile high-voltage generator and sensor system. To study the impedance dynamics in real time, the use of pulses of reduced voltage, below the threshold of reversible electroporation, is tested to characterise the state-of-the-treatment without interfering with it. With this purpose, a generator that provides both low voltage for sense tissue changes and high voltage for irreversible electroporation (IRE) was developed. In conclusion, the characterisation of the effects of electroporation in vegetal tissue, combined with the real-time monitoring of the state-of-the-treatment, will enable the provision of safer and more effective treatments.

The Air Microelemental Pollution and Trees Health Status. A Case Study: Quantification of Air Pollution with Pb, Using Trees as Bioindicators

The identification of the urban areas where bigger air pollutants generally, and metallic pollutants, particularly, concentrations compared to limit allowed maximum concentration are recorded are important in elaboration and implementation of the plans and programmes of air quality management, but also for permanent updating of these and their alignment to the continuous realit y changes. In this context, we emphasize the importance of using the tree planted in urban areas, as bioindicators for identification of air pollution with heavy metals ( Cu, Pb, Cd, Zn, etc. ) , their tendency to accumulate in vegetal material, leaves, and/or needles, respectively, being well known. In majority of cases, the heavy metals environmental air pollution leads to lesions of the vegetal tissue of the trees, mainly in leaves. The research aims to identify the lead pollution by using, as bioindicators, the trees planted in neighbourhood of the stations of air quality monitoring in Cluj - Napoca town. The research objectives consist in identification of the trees as possible biomonitoring agents and quantification of lead accumulation in trees leaves, with the aim of identification the areas where the lead concentration in leaves is superior to the limit lead maximum allowed concentration. The study was developed in Cluj - Napoca, and samples were harvested from trees located in areas from neighbourhood of the air quality monitoring stations respectively: CLU-1 ( Aurel Vlaicu St. ) , CLU-2 ( Centre - area from neighbourhood of Nicolae Balcescu College ) , CLU-3 ( 1 Decembrie 1918 Boulevard) , and CLU-4 ( area around Expo Transilvania ) .

Sunflower micronutrient uptake curves

Most studies in the literature regarding the uptake of micronutrients [zinc (Zn), manganese (Mn), iron (Fe), copper (Cu) and boron (B) accumulation] by sunflower are old. For this reason, this work is aimed to establish curves of sunflower micronutrient accumulation in tropical edaphoclimatic conditions. The experiment was conducted in field on Rhodic Eutrudox, located at the experimental farm of Embrapa Soja in Londrina-PR, using the commercial hybrid BRS 191. The experimental design was completely randomized, with four replicates. The experimental units consisted of 14 lines with a spacing of 0.70 meter between lines and 25.0 meters in length, with final density of 42,858 plants per hectare. Plants samples were collected every 14 days after emergence. After each collection, plants were divided into petiole, leaf, stem, capitulum and achenes. They were dried until reaching constant weight. Then, dry matter was weighed and the concentrations of Zn, Mn, Fe, Cu, and B were determined in the vegetal tissue. Micronutrient uptake occurred faster in the phase initiated 42 days after emergence (DAE). Iron was the most absorbed micronutrient, accumulating higher amounts in the achenes as well as in relation to the total absorbed. Fe, Mn, Zn, B and Cu concentrations were 38.06, 19.68, 9.20, 8,62 and 4.27 mg plant-1, respectively.

Nutritional diagnosis for eucalypt by DRIS, M-DRIS, and CND

The evaluation of the nutritional status in eucalypt (Eucalyptus grandis W. Hill ex Maid.) forests through vegetal tissue analyses what reflects water and nutrient flows in the system, and represents a complementary tool to soil analysis can be helpful to raise and maintain the forest productivity at high levels. This study compared the use of the Diagnosis and Recommendation Integrated System (DRIS), Modified-DRIS (M-DRIS), and Compositional Nutrient Diagnosis (CND) diagnose methods in eucalypt stands in Central-Eastern Minas Gerais State, Brazil. Data of productivity and of N, P, K, Ca, and Mg leaf contents in 993 Eucalyptus grandis stands aging between 72 and 153 months, planted on six sites in 3 ´ 2 m spacing, were used. The nutritional status was diagnosed by the DRIS, M-DRIS, and CND methods, and validated by the chi-square (c²) test applied to the nutrients diagnosed as primary limiting by deficiency. These three methods were compared to each other based on the diagnosis concordance frequency (DCF) derived from the fertilization response potential (FRP) by the criteria considering each nutrient separately; from all (5) to none (0); and only the primary limiting nutrients by either deficiency or excess. The diagnosis concordance level among the methods was procedure-dependent, and varied according to the nutrient concentration in trees.