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In September 2014, a high rate of bulb rot (5-15% depending on producer) was reported across all cultivars developing early in the storage season in the onion producing region of southwestern Idaho. Spanish yellow onion bulbs cv. Vaquero displaying tan to light brown necrotic rot were obtained. The bulb rot originated in the neck and spread to successive scales (Figure 1). In August 2015, onion cv. Redwing and Vaquero were observed to have wet necrotic lesions developing on leaves in the field (Figure 2). Margins of necrotic tissue, 1-2 cm3, were excised, surface sterilized, plated on water agar medium and incubated at 24°C. Hyphal growth was sub-cultured from eight strains (A- D in 2014; E-H in 2015) to fresh potato dextrose agar to obtain pure cultures. Cultures were characteristic of Fusarium species as described by Nelson et al. (1983) with the presence of microconidia formed on polyphialides with macroconidia present. Primers ITS4-A1 and ITS5 primers (White et al. 1990); EF-1 and EF-2 (O'Donnell et al. 1998); and fRPB2-5F and fRPB2-7cR (Liu et al. 1999) were used to amplify regions of the ITS, elongation factor 1-α and the second largest subunit of DNA-directed RNA polymerase II. Amplicons were sequenced and analyzed using BLAST (https://www.ncbi.nlm.nih.gov/) and in combination using Pairwise DNA Alignment and Polyphasic Identification (http://www.westerdijkinstitute.nl/Fusarium/DefaultInfo.aspx?Page=Home) as described by O'Donnell et al. 2015. Analysis indicated that these strains are Fusarium proliferatum, which is part of the F. fujikuroi species complex (O'Donnell et al. 1998). Similarity (99.5%) was observed in pairwise analyses and the polyphasic identification clustering to representative F. proliferatum strain NRRL 22944 and others. Sequences were submitted to Genbank and registered accession numbers are found in Table 1. To complete Koch's postulates, cv. Vaquero onion bulbs were surface sterilized and injected with 3 × 105 microconidia into the shoulder of each bulb. Five bulbs were inoculated for each isolate, placed in a mesh bag, and incubated at 30°C in the dark. Five bulbs injected with sterile water and five non-inoculated bulbs served as controls. After 14 days, each bulb was sliced vertically down the center and inspected for rot. All eight strains induced tan to light brown necrotic rot symptoms in each inoculated bulb. No symptoms were observed for the water inoculated and the non-inoculated onion bulbs. A fungus was isolated from the necrotic tissue and confirmed to be F. proliferatum as described above. Ten µl aliquots containing 1 × 105 microconidia of F. proliferatum strains (C, E-H) were applied to leaves in triplicate of 12-week-old onion plants (cv. Vaquero) wounded with a 21-gauge needle. Water controls were included. Within three days lesions, with light chlorosis, began to form and quickly spread on the leaves. A fungus was isolated and confirmed to be F. proliferatum as described above. This is the first extensive description and identification of F. proliferatum causing bulb rot in storage in Idaho (Mohan et al. 1997). In addition, this is the first report of the fungus causing leaf infection in the field. These findings confirm F. proliferatum as the causal agent of the high incidence of bulb rot observed in 2014 and 2015. This bulb rot continues to occur in southwestern Idaho and since the pathogen can cause leaf infections growers are encouraged to be vigilant for both leaf lesions during the growing season and bulb rot in storage.
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The ear-EEG has emerged as a promising candidate for real-world wearable brain monitoring. While experimental studies have validated several applications of ear-EEG, the source-sensor relationship for neural sources from across the brain surface has not yet been established. In addition, modeling of the ear-EEG sensitivity to sources of artifacts is still missing. Through volume conductor modeling, the sensitivity of various configurations of ear-EEG is established for a range of neural sources, in addition to ocular artifact sources for the blink, vertical saccade, and horizontal saccade eye movements. Results conclusively support the introduction of ear-EEG into conventional EEG paradigms for monitoring neural activity that originates from within the temporal lobes, while also revealing the extent to which ear-EEG can be used for sources further away from these regions. The use of ear-EEG in scenarios prone to ocular artifacts is also supported, through the demonstration of proportional scaling of artifacts and neural signals in various configurations of ear-EEG. The results from this study can be used to support both existing and prospective experimental ear-EEG studies and applications in the context of sensitivity to both neural sources and ocular artifacts.
RESUMO
Objective.Smart hearing aids which can decode the focus of a user's attention could considerably improve comprehension levels in noisy environments. Methods for decoding auditory attention from electroencapholography (EEG) have attracted considerable interest for this reason. Recent studies suggest that the integration of deep neural networks (DNNs) into existing auditory attention decoding (AAD) algorithms is highly beneficial, although it remains unclear whether these enhanced algorithms can perform robustly in different real-world scenarios. Therefore, we sought to characterise the performance of DNNs at reconstructing the envelope of an attended speech stream from EEG recordings in different listening conditions. In addition, given the relatively sparse availability of EEG data, we investigate possibility of applying subject-independent algorithms to EEG recorded from unseen individuals.Approach.Both linear models and nonlinear DNNs were employed to decode the envelope of clean speech from EEG recordings, with and without subject-specific information. The mean behaviour, as well as the variability of the reconstruction, was characterised for each model. We then trained subject-specific linear models and DNNs to reconstruct the envelope of speech in clean and noisy conditions, and investigated how well they performed in different listening scenarios. We also established that these models can be used to decode auditory attention in competing-speaker scenarios.Main results.The DNNs offered a considerable advantage over their linear analogue at reconstructing the envelope of clean speech. This advantage persisted even when subject-specific information was unavailable at the time of training. The same DNN architectures generalised to a distinct dataset, which contained EEG recorded under a variety of listening conditions. In competing-speakers and speech-in-noise conditions, the DNNs significantly outperformed the linear models. Finally, the DNNs offered a considerable improvement over the linear approach at decoding auditory attention in competing-speakers scenarios.Significance.We present the first detailed study into the extent to which DNNs can be employed for reconstructing the envelope of an attended speech stream. We conclusively demonstrate that DNNs improve the reconstruction of the attended speech envelope. The variance of the reconstruction error is shown to be similar for both DNNs and the linear model. DNNs therefore show promise for real-world AAD, since they perform well in multiple listening conditions and generalise to data recorded from unseen participants.