The Role of Bacteria in Acute Oak Decline in South-West Poland.

The process of multifactorial oak disease has been of interest to scientists from all over the world for many years. Recently, a new phenomenon has been added to the model related to oak decline, acute oak dieback, which causes oak decline and was first described in the UK. This study presents research on this phenomenon in the area of the largest oak stand complex in Poland, the so-called Krotoszyn Plateau. This work was carried out in two stages. In the first stage, 54 trees were tested for the presence of bacteria using molecular biology (real-time PCR). Subsequently, a tissue fragment was taken for inoculation from the trees in which the presence of B. goodwinii and G. quercinecans bacteria was confirmed. The isolates obtained were used to test Koch's postulates and for biochemical analyses for Polish strains. In addition, the results obtained were also compared with the presence of A. biguttatus, which is considered a bacterial vector, which, in turn, confirmed that the bacteria responsible for the AOD phenomenon can also be present in trees not inhabited by this insect. The results obtained confirmed the presence of bacteria and their potential to cause necrosis in oaks, which fits into the model of the spiral disease that has been causing mass mortality of oaks in this naturally and economically valuable area since the 1980s.

Analysis of Wheat Grain Infection by Fusarium Mycotoxin-Producing Fungi Using an Electronic Nose, GC-MS, and qPCR.

Fusarium graminearum and F. culmorum are considered some of the most dangerous pathogens of plant diseases. They are also considerably dangerous to humans as they contaminate stored grain, causing a reduction in yield and deterioration in grain quality by producing mycotoxins. Detecting Fusarium fungi is possible using various diagnostic methods. In the manuscript, qPCR tests were used to determine the level of wheat grain spoilage by estimating the amount of DNA present. High-performance liquid chromatography was performed to determine the concentration of DON and ZEA mycotoxins produced by the fungi. GC-MS analysis was used to identify volatile organic components produced by two studied species of Fusarium. A custom-made, low-cost, electronic nose was used for measurements of three categories of samples, and Random Forests machine learning models were trained for classification between healthy and infected samples. A detection performance with recall in the range of 88-94%, precision in the range of 90-96%, and accuracy in the range of 85-93% was achieved for various models. Two methods of data collection during electronic nose measurements were tested and compared: sensor response to immersion in the odor and response to sensor temperature modulation. An improvement in the detection performance was achieved when the temperature modulation profile with short rectangular steps of heater voltage change was applied.

Analysis of the Response Signals of an Electronic Nose Sensor for Differentiation between Fusarium Species.

Fusarium is a genus of fungi found throughout the world. It includes many pathogenic species that produce toxins of agricultural importance. These fungi are also found in buildings and the toxins they spread can be harmful to humans. Distinguishing Fusarium species can be important for selecting effective preventive measures against their spread. A low-cost electronic nose applying six commercially available TGS-series gas sensors from Figaro Inc. was used in our research. Different modes of operation of the electronic nose were applied and compared, namely, gas adsorption and desorption, as well as modulation of the sensor's heating voltage. Classification models using the random forest technique were applied to differentiate between measured sample categories of four species: F. avenaceum, F. culmorum, F. greaminarum, and F. oxysporum. In our research, it was found that the mode of operation with modulation of the heating voltage had the advantage of collecting data from which features can be extracted, leading to the training of machine learning classification models with better performance compared to cases where the sensor's response to the change in composition of the measured gas was exploited. The optimization of the data collection time was investigated and led to the conclusion that the response of the sensor at the beginning of the heating voltage modulation provides the most useful information. For sensor operation in the mode of gas desorption/absorption (i.e., modulation of the gas composition), the optimal time of data collection was found to be longer.

Was it really different? COVID-19-pandemic period in long-term recreation monitoring - A case study from Polish forests.

The COVID -19 pandemic posed serious challenge for securing public health worldwide. Public health preparedness and restrictions put in place impacted many aspects of human life, including recreational activities and access to outdoor recreational destinations. Green spaces have become one of the few sources of resilience during the coronavirus crisis due to their restorative effects on psychophysical health and community well-being. The aim of this study is to analyse the impact of the COVID -19 pandemic on forest visitation. The results are based upon long-term visitor data acquired via pyroelectric sensors (Eco-Counter) in three forest districts located in Poland (Browsk, Gdansk & Kozienice Forest Districts). The analysis covers the period between January 01, 2019 and December 31, 2020 and the results confirm changes in recreational use in the studied forest areas during the pandemic compared to the preceding year. However, observed changes in forest visitation vary by pandemic period and study area. The ban on access to forest areas significantly reduced the number of forest visits in all studied areas. The number of visits to sub-urban forests (Gdansk Forest District) and to remote nature-based tourist destinations (Browsk Forest District) increased in the later pandemic periods, especially in the summer months of 2020, while it remained the same in a popular nearby recreation area: Kozienice Forest District. There were only minor temporal shifts in the distribution of weekly and daily visits. The results are important for public health preparedness planning in crisis situations and for provisioning conditions supporting societal health and well-being. Objective data on forest visits are necessary for successful management of forest areas and surrounding amenities. More cross-sector collaboration and public participation would be desirable to create sustainable, resilient, and liveable spaces for the society. Management Implications: •Long-term visitation monitoring is crucial for successful management of outdoor recreation destinations and their catchment areas.•Objective numbers concerning forest visitation from the pre-pandemic and COVID-19 pandemic period allow observing trends and making fact-based management decisions during period of crisis.•Changes in the investigated three forest study areas in Poland were not homogenous, which implies the necessity of systematic visitor monitoring in multiple destinations, in order to cover different types of forest areas and also local diversity in recreational use.•More intersectoral, interdisciplinary and transdisciplinary exchange would be desirable to better integrate existing on-site visitor monitoring data into decision making processes related to forest management, urban planning, transportation, tourism and public health.

Development of a Low-Cost Electronic Nose with an Open Sensor Chamber: Application to Detection of Ciboria batschiana.

In the construction of electronic nose devices, two groups of measurement setups could be distinguished when we take into account the design of electronic nose chambers. The simpler one consists of placing the sensors directly in the environment of the measured gas, which has an important advantage, in that the composition of the gas is not changed as the gas is not diluted. However, that has an important drawback in that it is difficult to clean sensors between measurement cycles. The second, more advanced construction, contains a pneumatic system transporting the gas inside a specially designed sensor chamber. A new design of an electronic nose gas sensor chamber is proposed, which consists of a sensor chamber with a sliding chamber shutter, equipped with a simple pneumatic system for cleaning the air. The proposal combines the advantages of both approaches to the sensor chamber designs. The sensors can be effectively cleared by the flow of clean air, while the measurements are performed in the open state when the sensors are directly exposed to the measured gas. Airflow simulations were performed to confirm the efficiency of clean air transport used for sensors' cleaning. The demonstrated electronic nose applies eight Figaro Co. MOS TGS series sensors, in which a transient response caused by a change of the exposition to measured gas, and change of heater voltage, was collected. The new electronic nose was tested as applied to the differentiation between the samples of Ciboria batschiana fungi, which is one of the most harmful pathogens of stored acorns. The samples with various coverage, thus various concentrations of the studied odor, were measured. The tested device demonstrated low noise and a good level of repetition of the measurements, with stable results during several hours of repetitive measurements during an experiment lasting five consecutive days. The obtained data allowed complete differentiation between healthy and infected samples.

Electronic Nose Differentiation between Quercus robur Acorns Infected by Pathogenic Oomycetes Phytophthora plurivora and Pythium intermedium.

Identification of the presence of pathogenic oomycetes in infected plant material proved possible using an electronic nose, giving hope for a tool to assist nurseries and quarantine services. Previously, species of Phytophthora plurivora and Pythium intermedium have been successfully distinguished in germinated acorns of English oak Quercus robur L. Chemical compound analyses performed by HS-SPME/GC-MS (Headspace Solid-Phase Microextraction/Gas Chromatography-Mass Spectrometry) revealed the presence of volatile antifungal molecules produced by oak seedlings belonging to terpenes and alkanes. Compounds characteristic only of Phytophthora plurivora or Pythium intermedium were also found. Methylcarveol occurred when germinated acorns were infected with Pythium, while neophytadiene (isomer 2 and 3) occurred only when infected with Phytophthora. Moreover, isopentanol was found in acorns infected with Phytophthora, while in control, isopentyl vinyl ether was not observed anywhere else. Among the numerous volatile compounds, isopentanol only occurred in acorns infected with Phytophthora and methylcarveol in acorns infected with Pythium.

Development of a Low-Cost Electronic Nose for Detection of Pathogenic Fungi and Applying It to Fusarium oxysporum and Rhizoctonia solani.

Electronic noses can be applied as a rapid, cost-effective option for several applications. This paper presents the results of measurements of samples of two pathogenic fungi, Fusarium oxysporum and Rhizoctonia solani, performed using two constructions of a low-cost electronic nose. The first electronic nose used six non-specific Figaro Inc. metal oxide gas sensors. The second one used ten sensors from only two models (TGS 2602 and TGS 2603) operating at different heater voltages. Sets of features describing the shapes of the measurement curves of the sensors' responses when exposed to the odours were extracted. Machine learning classification models using the logistic regression method were created. We demonstrated the possibility of applying the low-cost electronic nose data to differentiate between the two studied species of fungi with acceptable accuracy. Improved classification performance could be obtained, mainly for measurements using TGS 2603 sensors operating at different voltage conditions.

Application of a Low-Cost Electronic Nose for Differentiation between Pathogenic Oomycetes Pythium intermedium and Phytophthora plurivora.

Compared with traditional gas chromatography-mass spectrometry techniques, electronic noses are non-invasive and can be a rapid, cost-effective option for several applications. This paper presents comparative studies of differentiation between odors emitted by two forest pathogens: Pythium and Phytophthora, measured by a low-cost electronic nose. The electronic nose applies six non-specific Figaro Inc. metal oxide sensors. Various features describing shapes of the measurement curves of sensors' response to the odors' exposure were extracted and used for building the classification models. As a machine learning algorithm for classification, we use the Support Vector Machine (SVM) method and various measures to assess classification models' performance. Differentiation between Phytophthora and Pythium species has an important practical aspect allowing forest practitioners to take appropriate plant protection. We demonstrate the possibility to recognize and differentiate between the two mentioned species with acceptable accuracy by our low-cost electronic nose.