In normal upper airways, nitric oxide is generated by the paranasal sinus epithelium and then diffuses into the nasal cavities. This study examined whether or not nasal NO concentration is affected by paranasal sinus inflammatory diseases.The influence of obstruction (nasal polyposis) and/or inflammation (allergy or chronic sinusitis) of the paranasal sinuses on nasal NO concentration was evaluated in nasal allergic (n=7 patients) or nonallergic (n=20) polyposis, nonallergic chronic sinusitis (n=10) and Kartagener's syndrome (n=6) and compared with control subjects (n=42). A score of alteration of the paranasal sinus (number of altered and occluded sinuses) was determined by a computed tomography scan.The nasal NO concentration in nasal nonallergic polyposis (15020 parts per billion (ppb)) was significantly decreased compared with both controls (2236 ppb, p=0.01) and polyposis with allergy (27228 ppb, p<0.0001). In each group, the nasal NO concentration was inversely correlated with the extent of tomodensitometric alteration of the paranasal sinuses. In Kartagener's syndrome, the nasal NO concentration (142 ppb) was drastically decreased compared with all other groups, despite the presence of open paranasal sinuses.Thus, the nasal NO concentration in patients with nasal polyposis appeared to be dependent on both the allergic status and the degree of obstruction of the paranasal sinuses. Eur Respir J 1999; 13: 307±312. The discovery that mammalian cells generate nitric oxide, a free radical gas previously considered merely as an atmospheric pollutant, is providing important information about many biological processes. NO is generated from arginine by a family of enzymes, the NO synthases (NOS), and acts as an autocrine and paracrine messenger [1,2]. NO also plays a major role in nonspecific host defence as a result of its antiviral and antibacterial properties. Type II NOS, initially characterized in the rodent macrophage, has been shown to be expressed only after induction by pro-inflammatory cytokines or bacterial lipopolysaccharide [1]. This isoform can be expressed by most cells involved in inflammation and is also called inducible NOS [3].Recently, LUNDBERG and coworkers [4±6] have clearly shown that most of the NO in the exhaled air of healthy subjects originates from the upper respiratory tract, with only a minor contribution from the lower airways. A type II NOS, mainly expressed in the epithelium of the paranasal sinuses, would account for most of this NO production [6]. Thus, NO could play a critical role in the physiology and pathology of the upper respiratory tract because, in addition to its role in immunity and host defence [3], NO stimulates ciliary motility [7]. Interestingly, LUNDBERG et al. [8] showed that patients with Kartagener's syndrome (referred to as an immobile cilia syndrome and characterized by situs inversus, sinusitis and bronchiectasis) had very low nasal NO concentrations. The present authors [9] and others [10] have recently shown that the nasal NO concentration in patie...

The current mainstream approach of using manual measurements and visual inspections for crop lodging detection is inefficient, time-consuming, and subjective. An innovative method for wheat lodging detection that can overcome or alleviate these shortcomings would be welcomed. This study proposed a systematic approach for wheat lodging detection in research plots (372 experimental plots), which consisted of using unmanned aerial systems (UAS) for aerial imagery acquisition, manual field evaluation, and machine learning algorithms to detect the occurrence or not of lodging. UAS imagery was collected on three different dates (23 and 30 July 2019, and 8 August 2019) after lodging occurred. Traditional machine learning and deep learning were evaluated and compared in this study in terms of classification accuracy and standard deviation. For traditional machine learning, five types of features (i.e. gray level co-occurrence matrix, local binary pattern, Gabor, intensity, and Hu-moment) were extracted and fed into three traditional machine learning algorithms (i.e., random forest (RF), neural network, and support vector machine) for detecting lodged plots. For the datasets on each imagery collection date, the accuracies of the three algorithms were not significantly different from each other. For any of the three algorithms, accuracies on the first and last date datasets had the lowest and highest values, respectively. Incorporating standard deviation as a measurement of performance robustness, RF was determined as the most satisfactory. Regarding deep learning, three different convolutional neural networks (simple convolutional neural network, VGG-16, and GoogLeNet) were tested. For any of the single date datasets, GoogLeNet consistently had superior performance over the other two methods. Further comparisons between RF and GoogLeNet demonstrated that the detection accuracies of the two methods were not significantly different from each other (p > 0.05); hence, the choice of any of the two would not affect the final detection accuracies. However, considering the fact that the average accuracy of GoogLeNet (93%) was larger than RF (91%), it was recommended to use GoogLeNet for wheat lodging detection. This research demonstrated that UAS RGB imagery, coupled with the GoogLeNet machine learning algorithm, can be a novel, reliable, objective, simple, low-cost, and effective (accuracy > 90%) tool for wheat lodging detection.

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