Characterization of three-nucleate Rhizoctonia AG-E based on their morphology and phylogeny.

The genus Rhizoctonia has been classified into two main groups according to the number of nuclei. Binucleate Rhizoctonia strains have two nuclei in each cell, whereas multinucleate Rhizoctonia fungi were observed to have a variable number of nuclei ranging from 4 to 16 in each cell. In the study, twelve Polish isolates were tested. According to ITS1-5,8S-ITS2 rDNA sequences, the isolates were classified in the AG-E. Their affiliation to AG was confirmed by anastomosis reactions with tester isolates. The number of nuclei was counted with DAPI staining under a fluorescent microscope, and the diameter of the hyphae was also measured. Not all AG-E isolates had the same number of nuclei in their cells: one group among these fungi produced cells with a diverse number of nuclei, usually 3; however, this number ranged from 2 to 4, making the average number of nuclei close to 3. It can be assumed that all isolates with three nuclei belong to this group, which may greatly facilitate the preliminary identification of trinucleate isolates of Rhizoctonia spp. belonging to AG-E. Based on these characters, we call these isolates AG-E-3n isolates. The thiamine requirement is not helpful in classifying and describing the AG-E strains.

Early detection of Solanum lycopersicum diseases from temporally-aggregated hyperspectral measurements using machine learning.

Some plant diseases can significantly reduce harvest, but their early detection in cultivation may prevent those consequential losses. Conventional methods of diagnosing plant diseases are based on visual observation of crops, but the symptoms of various diseases may be similar. It increases the difficulty of this task even for an experienced farmer and requires detailed examination based on invasive methods conducted in laboratory settings by qualified personnel. Therefore, modern agronomy requires the development of non-destructive crop diagnosis methods to accelerate the process of detecting plant infections with various pathogens. This research pathway is followed in this paper, and an approach for classifying selected Solanum lycopersicum diseases (anthracnose, bacterial speck, early blight, late blight and septoria leaf) from hyperspectral data captured on consecutive days post inoculation (DPI) is presented. The objective of that approach was to develop a technique for detecting infection in less than seven days after inoculation. The dataset used in this study included hyperspectral measurements of plants of two cultivars of S. lycopersicum: Benito and Polfast, which were infected with five different pathogens. Hyperspectral reflectance measurements were performed using a high-spectral-resolution field spectroradiometer (350-2500 nm range) and they were acquired for 63 days after inoculation, with particular emphasis put on the first 17 day-by-day measurements. Due to a significant data imbalance and low representation of measurements on some days, the collective datasets were elaborated by combining measurements from several days. The experimental results showed that machine learning techniques can offer accurate classification, and they indicated the practical utility of our approaches.

Rhizoctonia solani AG 11 isolated for the first time from sugar beet in Poland.

Two isolates of Rhizoctonia solani AG11 were isolated from sugar beet seedlings from South-west Poland. Both isolates gave C2 reactions in anastomose pairings with the tester isolates of AG11. The membership of both isolates to AG11 was confirmed by analysis of pectic isozyme profiles, and by verification that the internal transcribed spacer sequences of both isolates matched the references in the GenBank database. Both AG11 isolates formed white-beige to creamy-colored mycelium with wide concentric zonation. One of them formed light-colored sclerotia. The average daily rate of hyphal growth at 21 °C was 22.8 mm and 22.6 mm on PDA. They were mildly pathogenic to sugar beet seedlings due to the mycelial and secondary metabolites' activity. The sensitivity to fungicides typically used in sugar beet protection was different for each isolate; one of them (isolate ID11) was less sensitive to thiram than the other (isolate ID3). This article discusses the worldwide occurrence of R. solani AG11, expands the currently known host range, shows its broad world distribution in regions of moderate climate, and confirms the isolates' low frequency.

Estimation of the committed radiation dose resulting from gamma radionuclides ingested with food.

The objective of the study was to estimate the value of the radiation dose absorbed in consequence of consumption of popular food products for individual age groups. Potatoes, corn and sugar beet were selected for the study. Edible parts of these plants were collected in experimental fields of the KWS Lochów Polska Sp. z o.o. seeding company in Kondratowice (Poland). On the basis of the obtained study results, it can be stated that in consequence of consumption of the selected food products, people may receive increased doses from both natural and artificial radioactive isotopes. The doses calculated for several age groups do not show any health hazards in consequence of consumption of the tested food. One of the determined radionuclides was 137Cs; however, its presence in the absorbed dose is lower than the doses from natural radioactive isotopes, in particular 40K.