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IntroductionCough is a common symptom of respiratory diseases, and prolonged monitoring of cough can help assist doctors in making judgments about patients’ conditions, among which cough frequency is an indicator that characterizes the state of the patient’s lungs. Therefore, the aim of this paper is to design an automatic cough counting system to monitor the number of coughs per minute for a long period of time.MethodsIn this paper, a complete cough counting process is proposed, including denoising, segment extraction, eigenvalue calculation, recognition, and counting process; and a wearable automatic cough counting device containing acquisition and reception software. The design and construction of the algorithm is based on realistically captured cough-containing audio from 50 patients, combined with short-time features, and Meier cepstrum coefficients as features characterizing the cough.ResultsThe accuracy, sensitivity, specificity, and F1 score of the method were 93.24%, 97.58%, 86.97%, and 94.47%, respectively, with a Kappa value of 0.9209, an average counting error of 0.46 counts for a 60-s speech segment, and an average runtime of 2.80 ± 2.27 s.DiscussionThis method improves the double threshold method in terms of the threshold and eigenvalues of the cough segments’ sensitivity and has better performance in terms of accuracy, real-time performance, and computing speed, which can be applied to real-time cough counting and monitoring in small portable devices with limited computing power. The developed wearable portable automatic cough counting device and the accompanying host computer software application can realize the long-term monitoring of patients’ coughing condition.

AbstractObjective Dupilumab has been approved for the treatment of severe asthma with type 2 inflammation by inhibiting interleukin (IL)-4 and IL-13 signaling. However, dupilumab-induced hypereosinophilia (HE) has been reported and should not be ignored. The aim of this study was to investigate the efficacy of dupilumab in Chinese patients with severe asthma, whether HE affects its efficacy, and the possible risk factors for HE.Methods 20 patients with severe asthma who received dupilumab treatment for at least 12 months in the First Affiliated Hospital of Guangzhou Medical University from 2019 to 2022 were included. We compared clinical data and laboratory tests results before dupilumab treatment and at 4 and 12 months after treatment. Based on whether dupilumab treatment triggers HE defined as blood eosinophil count (BEC) ≥ 1.5 × 109 cells/L, the patients were allocated into non-HE and HE groups.Results The patients showed a significant increase in asthma control test (ACT) scores, a decrease in the number of exacerbations, a decrease in the proportion of patients taking an oral corticosteroid (OCS) and in the dose, and a significant improvement in the pulmonary function parameters FEV1/FVC (%) and FEV1 (% predicted) after 4 and 12 months of treatment with dupilumab. For type 2 inflammatory biomarkers, the levels of fractional concentration of exhaled nitric oxide (FeNO), sputum eosinophil count percentage (SEC%) and total immunoglobulin E (TIgE) decreased significantly, whereas BEC were higher after 4 months of treatment, but returned to baseline levels after 12 months. 8 patients (40%) developed asymptomatic HE after dupilumab, and the efficacy was not significantly different between the HE and non-HE groups. The earliest BEC elevation appeared at 1 month after treatment, but most of them declined after 6 months, and basically returned to the baseline level around 12 months of treatment. In addition, we further found that when patients had FeNO ≥ 60 ppb, food allergens positive and combined eosinophilic otitis media (EOM), their BEC increased significantly more than that of the control group after 4 months as well as 12 months of treatment.Conclusions This study demonstrated that dupilumab was efficacious in Chinese patients with severe asthma, and some patients developed asymptomatic, self-limited HE, which did not affect its efficacy. Additionally, FeNO ≥60 ppb, food allergens positive, and co-morbidities with EOM may be the risk factors for developing HE.

BackgroundTo quantify the changes in dynamic visual acuity (DVA) and explain the hidden reasons after acute exposure to hypobaric hypoxia status.MethodsThe study group comprised 18 healthy male and 15 healthy female participants aged 20–24 years old. DVA was measured with the self-developed software of Meidixin (Tianjin) Co., Ltd. Measurements were taken at eight altitudes. Data analysis was performed using the Kolmogorov–Smirnov test, paired sample T-test, and two-way repeated measures analysis of variance (ANOVA) for repeated measurements.ResultsAt constant altitude, DVA showed an overall decreasing trend with increasing angular velocity and a fluctuating decrease at the vast majority of altitudes. At constant angular velocities, DVA gradually increased with altitude, with the most pronounced increase in DVA at altitude 5, and thereafter a gradual decrease in DVA as altitude increased. Finally, as altitude decreased, DVA increased again and reached a higher level at the end of the experiment, which was superior to the DVA in the initial state.ConclusionUnder a hypobaric hypoxic environment at high altitude, DVA was affected by the angular velocity and the degree of hypoxia, manifesting as an increase or decrease in DVA, which affects the pilot's observation of the display and control interfaces during the driving process, acquisition of information, and decision-making ability, which in turn may potentially jeopardize the safety of the flight.

This paper addresses the issue of pesticide waste and low utilization rates resulting from traditional plant protection via spraying operations, which apply equal dosages to different targets or to different parts of the same target. To tackle this problem, we designed a variable fruit tree spraying system based on the ExG-AABB (excess green and axis-aligned bounding box) algorithm. We used a Kinect depth camera to capture information about the fruit tree canopy and constructed a spray flow model using pulse width modulation and variable spray control technology. Variable multi-nozzle spraying was guided by combining this canopy data. We evaluated the accuracy of each model in calculating canopy volume by comparing the coefficient of determination (R2) and root mean square error (RMSE) of the ExG-AABB with the slice convex hull method, voxel method, three-dimensional alpha-shape method, and QuickHull method. The ExG-AABB algorithm had the highest R2 value (0.9334) and the lowest RMSE value (0.0353 m3) among the five models, indicating that it most accurately reflects the true volume of the fruit tree canopy. This validates the effectiveness of the ExG-AABB algorithm in calculating canopy volume. We established a correlation model between canopy volume and spray volume, designed a canopy-adaptive layering method based on point cloud processing, and achieved precise calculation of nozzle flow. Comparative field experiments were conducted to analyze the spray coverage rate and observed flow, thereby evaluating the spraying effect of this variable spraying system. The experimental results showed that compared to conventional continuous spraying, this variable spraying system not only achieves more uniform spray coverage but also significantly reduces pesticide usage by 48.1%. Furthermore, through system optimization, the average coverage rate of the middle layer of the canopy decreased by 17.53%, effectively reducing the phenomenon of overlapping spraying from multiple nozzles and improving spraying efficiency.

Pests are widely distributed in nature, characterized by their small size, which, along with environmental factors such as lighting conditions, makes their identification challenging. A lightweight pest detection network, HCFormer, combining convolutional neural networks (CNNs) and a vision transformer (ViT) is proposed in this study. Data preprocessing is conducted using a bottleneck-structured convolutional network and a Stem module to reduce computational latency. CNNs with various kernel sizes capture local information at different scales, while the ViT network’s attention mechanism and global feature extraction enhance pest feature representation. A down-sampling method reduces the input image size, decreasing computational load and preventing overfitting while enhancing model robustness. Improved attention mechanisms effectively capture feature relationships, balancing detection accuracy and speed. The experimental results show that HCFormer achieves 98.17% accuracy, 91.98% recall, and a mean average precision (mAP) of 90.57%. Compared with SENet, CrossViT, and YOLOv8, HCFormer improves the average accuracy by 7.85%, 2.01%, and 3.55%, respectively, outperforming the overall mainstream detection models. Ablation experiments indicate that the model’s parameter count is 26.5 M, demonstrating advantages in lightweight design and detection accuracy. HCFormer’s efficiency and flexibility in deployment, combined with its high detection accuracy and precise classification, make it a valuable tool for identifying and classifying crop pests in complex environments, providing essential guidance for future pest monitoring and control.

Objectives We assessed the efficacy of a 3-week primary or salvage caspofungin regimen in patients with chronic obstructive pulmonary disease (COPD) and concomitant proven or suspected invasive pulmonary aspergillosis (IPA). Methods Forty-four patients were treated with an initial loading caspofungin dose of 70 mg, followed by a daily dose of 50 mg for 20 days. The main efficacy endpoint was clinical effectiveness. Secondary endpoints included the clinical efficacy of caspofungin after 1 week, therapeutic efficacy based on the European Organization for Research and Treatment of Cancer and Mycoses Study Group Education and Research Consortium (EORTC/MSG) criteria, the sensitivity of different Aspergillus strains to caspofungin in vitro , and the safety of caspofungin. Results An assessment of 42 patients in the intention-to-treat group revealed efficacy rates of 33.33% within 1 week and 38.10% within 3 weeks. According to the EORTC/MSG criteria, the treatment success rate was 38.10%. The success rate of first-line treatment was 54.76%, whereas salvage treatment had a success rate of 45.24%. No adverse events were reported among the participants. Conclusions Caspofungin is effective and safe as an initial or salvage treatment for patients with IPA and COPD.

BackgroundCough is a common symptom during and after COVID-19 infection; however, few studies have described the cough profiles of COVID-19. ObjectiveThe aim of this study was to investigate the prevalence, severity, and associated risk factors of severe and persistent cough in individuals with COVID-19 during the latest wave of the Omicron variant in China. MethodsIn this nationwide cross-sectional study, we collected information of the characteristics of cough from individuals with infection of the SARS-CoV-2 Omicron variant using an online questionnaire sent between December 31, 2022, and January 11, 2023. ResultsThere were 11,718 (n=7978, 68.1% female) nonhospitalized responders, with a median age of 37 (IQR 30-47) years who responded at a median of 16 (IQR 12-20) days from infection onset to the time of the survey. Cough was the most common symptom, occurring in 91.7% of participants, followed by fever, fatigue, and nasal congestion (68.8%-87.4%). The median cough visual analog scale (VAS) score was 70 (IQR 50-80) mm. Being female (odds ratio [OR] 1.31, 95% CI 1.20-1.43), having a COVID-19 vaccination history (OR 1.71, 95% CI 1.37-2.12), current smoking (OR 0.48, 95% CI 0.41-0.58), chronic cough (OR 2.04, 95% CI 1.69-2.45), coronary heart disease (OR 1.71, 95% CI 1.17-2.52), asthma (OR 1.22, 95% CI 1.02-1.46), and gastroesophageal reflux disease (GERD) (OR 1.21, 95% CI 1.01-1.45) were independent factors for severe cough (VAS>70, 37.4%). Among all respondents, 35.0% indicated having a productive cough, which was associated with risk factors of being female (OR 1.44, 95% CI 1.31-1.57), having asthma (OR 1.84, 95% CI 1.52-2.22), chronic cough (OR 1.44, 95% CI 1.19-1.74), and GERD (OR 1.22, 95% CI 1.01-1.47). Persistent cough (>3 weeks) occurred in 13.0% of individuals, which was associated with the risk factors of having diabetes (OR 2.24, 95% CI 1.30-3.85), asthma (OR 1.70, 95% CI 1.11-2.62), and chronic cough (OR 1.97, 95% CI 1.32-2.94). ConclusionsCough is the most common symptom in nonhospitalized individuals with Omicron SARS-CoV-2 variant infection. Being female, having asthma, chronic cough, GERD, coronary heart disease, diabetes, and a COVID-19 vaccination history emerged as independent factors associated with severe cough, productive cough, and persistent cough.

Abstract Rationale Chronic obstructive pulmonary disease (COPD) is a complicated chronic inflammatory disease. It is important to investigate the characteristics of acute exacerbation of COPD to develop new therapeutic strategies. Objective This study aimed to determine the relationship between the human beta-defensin-2 (hBD-2) levels and aggravation of COPD. Methods We detected the sputum hBD-2 level of 254 patients from Guangzhou, China, for 2 years. The study participants were categorized into the COPD group (n = 203, GOLD 0–4) and the control group (n = 51, 40–79 years old). At baseline, 12th month, and 24th month, we detected the sputum hBD-2 level and levels of cytokines, such as CXCL10, CXCL11, and IFN. Results At baseline, there were no significant differences in the sputum and serum hBD-2 levels between the patients and the controls. However, the sputum hBD-2 levels of patients who had at least one symptom aggravation over the next 2 years were significantly lower than those of patients without any exacerbations (1130.9 ± 858.4 pg/mL vs. 2103.7 ± 1294.2 pg/mL, respectively; p = 0.001). Nevertheless, there were no statistically significant differences in the sputum hBD-2 levels between patients (no aggravation history) and controls (2084.9 ± 1317.6 pg/mL vs. 2152.5 ± 1251.6 pg/mL, respectively; p = 0.626). We used a logistic regression model to assess the relationship between aggravation and sputum hBD-2 levels. Interestingly, we found that low hBD-2 level (

Due to the challenges of pest detection in complex environments, this research introduces a lightweight network for tobacco pest identification leveraging enhancements in YOLOv8 technology. Using YOLOv8 large (YOLOv8l) as the base, the neck layer of the original network is replaced with an asymptotic feature pyramid network (AFPN) network to reduce model parameters. A SimAM attention mechanism, which does not require additional parameters, is incorporated to improve the model’s ability to extract features. The backbone network’s C2f model is replaced with the VoV-GSCSP module to reduce the model’s computational requirements. Experiments show the improved YOLOv8 model achieves high overall performance. Compared to the original model, model parameters and GFLOPs are reduced by 52.66% and 19.9%, respectively, while mAP@0.5 is improved by 1%, recall by 2.7%, and precision by 2.4%. Further comparison with popular detection models YOLOv5 medium (YOLOv5m), YOLOv6 medium (YOLOv6m), and YOLOv8 medium (YOLOv8m) shows the improved model has the highest detection accuracy and lightest parameters for detecting four common tobacco pests, with optimal overall performance. The improved YOLOv8 detection model proposed facilitates precise, instantaneous pest detection and recognition for tobacco and other crops, securing high-accuracy, comprehensive pest identification.

Predicting student performance is a crucial research area in educational data mining. Student grades are influenced by various factors such as past academic performance, family background, and personal achievements. However, it is difficult to collect many relevant factors other than past academic performance, and it is not ideal to predict student performance using past academic records alone. Therefore, reducing the difficulty of data collection while maintaining timeliness and prediction accuracy remains a pressing issue. To address this challenge, this study proposes a novel model for predicting university student performance based on multi-feature fusion and attention mechanisms. The proposed model focuses on analyzing historical academic grades from multiple dimensions among university students to extract features that reveal relationships between courses and students, among different students themselves, or even between courses themselves. Additionally, an attention mechanism is introduced to explore the relationship between different dimensional features. This study collected a triplet set of related courses and students’ real historical grades, proved the correlation between courses through data analysis, and verified the effectiveness of different dimensional features. Experimental results show that, compared with traditional machine learning methods, our proposed method achieves better prediction accuracy with a precision rate of 72.5%.

Background: The characteristics of patients with eosinophilia are heterogeneous and the outcomes can vary from asymptomatic to severe. Objective: To describe the feature of patients with eosinophilia in a single center. Design: Based on the electronic medical records from Yangjiang People's Hospital in China, the inpatients admitted between June 2018 and February 2021 with measured blood eosinophil counts were evaluated. Methods: Eosinophilia was defined as a peripheral blood eosinophil count of ≥0.5 × 109/L. Differences were compared by eosinophilia severity. The medical records of patients with moderate to severe eosinophilia were reviewed and summarized in terms of examination, diagnoses and management. And these patients were matched with patients without incidental eosinophilia by propensity score and the differences were compared. Results: A total of 7,835 patients with eosinophilia were identified out of 131,566 total inpatients. All types of eosinophilia were most common in males (8.2%; 5,351/65,615), and in patients aged 0–6 years (11.6%; 1,760/15,204), and in the pediatric (10.8%; 1,764/16,336) department, followed by dermatology (10.6%; 123/1,162), Oncology (7.5%; 394/5,239) and Intensive care unit (ICU) (7.4%; 119/1,608). Patients with moderate to severe eosinophilia were more likely to admit to ICU (moderate: 1.3%; severe: 0.50%). In patients with moderate to severe eosinophilia, only 205/621 (33%) had eosinophilia mentioned in their records, and only 63/621 (10.1%) underwent investigations for eosinophilia. The majority of patients with moderate to severe eosinophilia (372/621, 59.9%) had an infectious disease, and little examination (7.4%; 46/621) was taken to identify the cause of eosinophilia, and only 39/621 (6.3%) of patients had a discrete cause of eosinophilia identified. Patients with moderate to severe eosinophilia (24.3%; 151/621) exhibited certain chance to have organ dysfunction. Conclusion: Incidental eosinophilia in inpatients was frequently neglected and less investigated. Multidisciplinary consultation may improve outcomes of inpatients with moderate to severe eosinophilia.

The relative content of chlorophyll, assessed through the soil and plant analyzer development (SPAD), serves as a reliable indicator reflecting crop photosynthesis and the nutritional status during crop growth and development. In this study, we employed machine learning methods utilizing unmanned aerial vehicle (UAV) multi-spectrum remote sensing to predict the SPAD value of litchi fruit. Input features consisted of various vegetation indices and texture features during distinct growth periods, and to streamline the feature set, the full subset regression algorithm was applied for dimensionality reduction. Our findings revealed the superiority of stacking models over individual models. During the litchi fruit development period, the stacking model, incorporating vegetation indices and texture features, demonstrated a validation set coefficient of determination (R2) of 0.94, a root mean square error (RMSE) of 2.4, and a relative percent deviation (RPD) of 3.0. Similarly, in the combined litchi growing period and autumn shoot period, the optimal model for estimating litchi SPAD was the stacking model based on vegetation indices and texture features, yielding a validation set R2, RMSE, and RPD of 0.84, 3.9, and 1.9, respectively. This study furnishes data support for the precise estimation of litchi SPAD across different periods through varied combinations of independent variables.

IntroductionCanopy stomatal conductance (Sc) indicates the strength of photosynthesis and transpiration of plants. In addition, Sc is a physiological indicator that is widely employed to detect crop water stress. Unfortunately, existing methods for measuring canopy Sc are time-consuming, laborious, and poorly representative.MethodsTo solve these problems, in this study, we combined multispectral vegetation index (VI) and texture features to predict the Sc values and used citrus trees in the fruit growth period as the research object. To achieve this, VI and texture feature data of the experimental area were obtained using a multispectral camera. The H (Hue), S (Saturation) and V (Value) segmentation algorithm and the determined threshold of VI were used to obtain the canopy area images, and the accuracy of the extraction results was evaluated. Subsequently, the gray level co-occurrence matrix (GLCM) was used to calculate the eight texture features of the image, and then the full subset filter was used to obtain the sensitive image texture features and VI. Support vector regression, random forest regression, and k-nearest neighbor regression (KNR) Sc prediction models were constructed, which were based on single and combined variables.ResultsThe analysis revealed the following: 1) the accuracy of the HSV segmentation algorithm was the highest, achieving more than 80%. The accuracy of the VI threshold algorithm using excess green was approximately 80%, which achieved accurate segmentation. 2) The citrus tree photosynthetic parameters were all affected by different water supply treatments. The greater the degree of water stress, the lower the net photosynthetic rate (Pn), transpiration rate (Tr), and Sc of the leaves. 3) In the three Sc prediction models, The KNR model, which was constructed by combining image texture features and VI had the optimum prediction effect (training set: R2 = 0.91076, RMSE = 0.00070; validation set; R2 = 0.77937, RMSE = 0.00165). Compared with the KNR model, which was only based on VI or image texture features, the R2 of the validation set of the KNR model based on combined variables was improved respectively by 6.97% and 28.42%.DiscussionThis study provides a reference for large-scale remote sensing monitoring of citrus Sc by multispectral technology. Moreover, it can be used to monitor the dynamic changes of Sc and provide a new technique for gaining a better understanding of the growth status and water stress of citrus crops.

There are few data regarding adult protracted bacterial bronchitis (PBB). This study aimed to delineate the clinical features of PBB and evaluate their potential diagnostic value in adults. We recruited 55 adult patients with PBB and selected randomly 220 patients with non-PBB as control. A diagnosis of PBB was considered if patients had a cough lasting ≥3 weeks, no abnormalities of chest computed tomography, positive bacterial culture in sputum and/or response well to oral moxifloxacin for 1–4 weeks. The clinical manifestations and laboratory investigations were compared between PBB patients and non-PBB patients. Of the 55 patients with PBB, approximately three-fifths (34, 61.8%) were females with a median age of 46.0 years, which were similar to that of patients with non-PBB. We observed a shorter cough duration in PBB than non-PBB (median 3.0 versus 24.0 months, p < 0.001). Compared to non-PBB patients, PBB patients had higher incidences of productive cough, yellow phlegm and a sensation of mucus in the throat (SMIT) (all p < 0.001). Sputum neutrophils and lymphocytes were markedly elevated in PBB patients than non-PBB patients (both p = 0.004). Bacterial pathogens were detected in eight (28.6%) of 28 cases with PBB. The multivariate analyses showed yellow phlegm, productive cough, SMIT, increased sputum lymphocytes (≥2.3%) and cough duration ≤8.5 months with moderate sensitivity (50.9–81.8%) and moderate-high specificity (60.5–94.4%) for determining PBB. In summary, adults with PBB are characterized by productive cough, yellow phlegm, SMIT and neutrophilic airway inflammation. These cough features and increased sputum lymphocytes may be useful to indicate PBB.

Background: Patients with incidental eosinophilia is becoming increasingly common in clinical practice. But it remains challenging to diagnose and treat owing to its complex etiology. The awareness of physicians and the strategies of diagnosis and treatment toward eosinophilia are still unclear. Objective: We aimed to evaluate attention, diagnosis, treatment, and awareness of eosinophilia among physicians, as well as factors influencing clinical practice, and to find ways to improve the efficacy of this disease. Design: This is a cross-sectional survey. Methods: A cross-sectional study was conducted from 1 to 4 June 2021 in a tertiary hospital. Self-administered and validated electronic questionnaire was used to investigate the attention toward eosinophilia, the strategies of diagnosis and treatment, and the awareness in physicians. Results: A total of 607 valid questionnaires were collected, with a response rate of 84.5%. Among the responders, 65.4% of physicians claimed to be familiar with patients with eosinophilia but only 11.0% of them had read the relevant guidelines or expert consensus. Among 207 physicians who had ever diagnosed patients with eosinophilia, only 19.4% had performed detailed examinations. The accuracy of awareness questions was 1.6–53.5%, and only 26.5% of physicians had high levels of awareness. An increase in the awareness level of up to 2.82 folds was seen among physicians with factors such as job title, encountering patients with eosinophilia, linking patients’ conditions to peripheral blood eosinophil count, and paying attention to guidelines. Conclusion: This study highlighted the importance of raising awareness and knowledge of eosinophilia among physicians in China. More works on education about eosinophilia guideline are needed, which may help physicians make decision with more benefits to patients.

Crop classification of large-scale agricultural land is crucial for crop monitoring and yield estimation. Hyperspectral image classification has proven to be an effective method for this task. Most current popular hyperspectral image classification methods are based on image classification, specifically on convolutional neural networks (CNNs) and recurrent neural networks (RNNs). In contrast, this paper focuses on methods based on semantic segmentation and proposes a new transformer-based approach called HyperSFormer for crop hyperspectral image classification. The key enhancement of the proposed method is the replacement of the encoder in SegFormer with an improved Swin Transformer while keeping the SegFormer decoder. The entire model adopts a simple and uniform transformer architecture. Additionally, the paper introduces the hyper patch embedding (HPE) module to extract spectral and local spatial information from the hyperspectral images, which enhances the effectiveness of the features used as input for the model. To ensure detailed model processing and achieve end-to-end hyperspectral image classification, the transpose padding upsample (TPU) module is proposed for the model’s output. In order to address the problem of insufficient and imbalanced samples in hyperspectral image classification, the paper designs an adaptive min log sampling (AMLS) strategy and a loss function that incorporates dice loss and focal loss to assist model training. Experimental results using three public hyperspectral image datasets demonstrate the strong performance of HyperSFormer, particularly in the presence of imbalanced sample data, complex negative samples, and mixed sample classes. HyperSFormer outperforms state-of-the-art methods, including fast patch-free global learning (FPGA), a spectral–spatial-dependent global learning framework (SSDGL), and SegFormer, by at least 2.7% in the mean intersection over union (mIoU). It also improves the overall accuracy and average accuracy values by at least 0.9% and 0.3%, respectively, and the kappa coefficient by at least 0.011. Furthermore, ablation experiments were conducted to determine the optimal hyperparameter and loss function settings for the proposed method, validating the rationality of these settings and the fusion loss function.

Litchi leaf diseases and pests can lead to issues such as a decreased Litchi yield, reduced fruit quality, and decreased farmer income. In this study, we aimed to explore a real-time and accurate method for identifying Litchi leaf diseases and pests. We selected three different orchards for field investigation and identified five common Litchi leaf diseases and pests (Litchi leaf mite, Litchi sooty mold, Litchi anthracnose, Mayetiola sp., and Litchi algal spot) as our research objects. Finally, we proposed an improved fully convolutional one-stage object detection (FCOS) network for Litchi leaf disease and pest detection, called FCOS for Litch (FCOS-FL). The proposed method employs G-GhostNet-3.2 as the backbone network to achieve a model that is lightweight. The central moment pooling attention (CMPA) mechanism is introduced to enhance the features of Litchi leaf diseases and pests. In addition, the center sampling and center loss of the model are improved by utilizing the width and height information of the real target, which effectively improves the model’s generalization performance. We propose an improved localization loss function to enhance the localization accuracy of the model in object detection. According to the characteristics of Litchi small target diseases and pests, the network structure was redesigned to improve the detection effect of small targets. FCOS-FL has a detection accuracy of 91.3% (intersection over union (IoU) = 0.5) in the images of five types of Litchi leaf diseases and pests, a detection rate of 62.0/ms, and a model parameter size of 17.65 M. Among them, the detection accuracy of Mayetiola sp. and Litchi algal spot, which are difficult to detect, reached 93.2% and 92%, respectively. The FCOS-FL model can rapidly and accurately detect five common diseases and pests in Litchi leaf. The research outcome is suitable for deployment on embedded devices with limited resources such as mobile terminals, and can contribute to achieving real-time and precise identification of Litchi leaf diseases and pests, providing technical support for Litchi leaf diseases’ and pests’ prevention and control.

Objective: Guidelines from different regions on the use of non-invasive ventilation in COVID-19 have generally been inconsistent. The aim of this systematic review was to appraise the quality and availability of guidelines, and whether non-invasive ventilation in the early stages of the pandemic is of importance. Design and method: Databases, including PubMed, Web of Science, and Cochrane Library, as well as websites of international organizations and gray literature, were searched up to June 23, 2020. The reference lists of eligible papers were also hand-searched. Results: A total of 26 guidelines met the inclusion criteria. According to the Appraisal of Guidelines for Research and Evaluation (AGREE) II instrument, the guidelines’ methodological quality was low. Among six domains, Rigour of Development and Editorial Independence were of the lowest quality. Given the lack of evidence from randomized clinical trials and the great variation between different regions, recommendations for non-invasive ventilation have generated considerable debate regarding the early stages of COVID-19. Conclusions: Improving the methodological quality of the guidelines should be a goal with regard to future pandemics. Additionally, better-designed randomized clinical trials are needed to resolve contradictions regarding the impact of non-invasive ventilation. PROSPERO registration number: CRD42020198410.

Citrus psyllid is the main vector of Huanglongbing, and as such, it is responsible for huge economic losses across the citrus industry. The small size of this pest, difficulties in data acquisition, and the lack of target detection algorithms suitable for complex occlusion environments inhibit detection of the pest. The present paper describes the construction of a standard sample database of citrus psyllid in multi-focal lengths and out-of-focus states in the natural environment. By integrating the attention mechanism and optimizing the key module of BottleneckCSP, YOLOv5s-BC, we have created an accurate detection algorithm for small targets. Based on YOLOv5s, our algorithm incorporates an SE-Net channel attention module into the Backbone network and improves the detection of small targets by guiding the algorithm to the channel characteristics of small-target information. At the same time, the BottleneckCSP module in the neck network is improved, and extraction of multiple features of recognition targets is improved by the addition of a normalization layer and SiLU activation function. Experimental results based on a standard sample database show the recognition accuracy (intersection over union (IoU) = 0.5) of the YOLOv5s-BC algorithm for citrus psyllid to be 93.43%, 2.41% higher than that of traditional YOLOv5s. The accuracy and recall rates are also increased by 1.31% and 4.22%, respectively. These results confirm that the YOLOv5s-BC algorithm has good generalization ability in the natural context of citrus orchards, and it offers a new approach for the control of citrus psyllid.

Background and ObjectiveGrowing evidence added to the results from observational studies of lung cancer patients exhibiting eosinophilia. However, whether eosinophils contributed to tumor immune surveillance or neoplastic evolution was unknown. This study aimed to analyze the causal association between eosinophilia and lung cancer.MethodsThe causal effect of eosinophil count on lung cancer from a genome-wide association study (GWAS) was investigated using the two-sample Mendelian randomization (MR) method. Secondary results according to different histological subtypes of lung cancer were also implemented. Meanwhile, we compared the measured levels of blood eosinophil counts among different subtypes of lung cancer from real-world data.ResultsThe median absolute eosinophilic count (unit: 109/L) [median (min, max): Lung adenocarcinoma 0.7 (0.5, 15); Squamous cell lung cancer 0.7 (0.5, 1.3); Small cell lung cancer 0.7 (0.6, 1.3); p = 0.96] and the median eosinophil to leukocyte ratio [median (min, max): Lung adenocarcinoma 8.7% (2.1, 42.2%); Squamous cell lung cancer 9.3% (4.1, 17.7%); Small cell lung cancer 8.9% (5.1, 24.1%); p = 0.91] were similar among different histological subtypes of lung cancer. MR methods indicated that eosinophilia may provide 28% higher risk for squamous cell lung cancer in East Asian [Weighted median method: odds ratio (OR) = 1.28, 95% CI: 1.04–1.57, p = 0.02].ConclusionOur study suggested that eosinophilia may be a potential causal risk factor in the progression of squamous cell lung cancer in East Asian.

Abstract Background Cough and airway eosinophilic inflammation has not been highlighted in hypereosinophilic syndrome (HES). Case presentation We report 2 further cases and reviewed the clinical features and treatment of HES present with cough from the literature. Both cases were middle age male, presenting with chronic cough, airway eosinophilic inflammation and hyper eosinophilia who have been previous misdiagnosed as cough-variant asthma and failed anti-asthma treatment. PDGFRA fusion gene was confirmed in one case, but not in the other case. Both had evidence of myeloproliferative features. The tyrosine kinase inhibitor, imatinib, resulted in complete resolution of eosinophilia and cough. By searching PubMed, we found 8 HES cohorts of 411 cases between 1975 and 2013, where the incidence of cough was 23.11%. Sixteen case reports of HES presented with cough as predominant or sole symptom, with nine male patients with positive PDGFRA fusion gene, who responded well to imatinib. Six of seven patients, who tested negative for the PDGFRA, responded to systemic glucocorticoids. Conclusions Cough and airway eosinophilic inflammation is common in some HES patients. PDGFRA+ HES patients present with chronic cough respond well to imatinib. Our case reports indicate that PDGFRA negative HES patients may respond to imatinib as well.

BackgroundThe AtyPical Asthma in China (APAC) cohort is a multi-center prospective, observational cohort set-up to investigate the clinical, pathophysiological features, prognosis, and mechanisms of cough variant asthma (CVA).ObjectivesTo present the characteristics of newly physician-diagnosed adults with CVA (n = 328) compared to mild-moderate classic asthma (CA, n = 206).Methods and Main ResultsCVA subjects showed a higher proportion of female (67.1 vs. 55.3%, P = 0.0084), abnormal laryngopharyngeal sensations (71 vs. 51%, p < 0.0001) than CA, but presented with near normal spirometry and higher methacholine PD20-FEV1 values [4.2 (1, 8.6) vs. 0.8 (0.4, 4.7), P < 0.0001]. Lower fractional exhaled nitric oxide (FENO) levels [38.5 (19.8, 72.5) vs. 53. (28.5, 92.2), P = 0.0019], blood eosinophil counts [0.2 (0.1, 0.4) vs. 0.3 (0.2, 0.5), P = 0.0014], and sputum eosinophils [2.3 (0.3, 8.0) vs. 12.2 (2, 34.5), p < 0.0001] were found in CVA. Despite lower total serum IgE levels in CVA, there was similar proportion of atopy in both groups. The prevalence of cough in CA was 86.4%, while CVA reported more severe cough on Visual Analog Scale, Cough Evaluation Test, and Leicester Cough Questionnaire, similar anxiety and depression scores but better asthma control scores as reflected by Asthma Control Test compared to CA. No correlation was found between cough assessment outcomes and sputum eosinophil count, blood eosinophil count, FENO, spirometry variables, or PD20-FEV1.ConclusionCough variant asthma is distinctive from classic asthma in regard to clinical features, lung function, and airway inflammation. Quality of life is badly impaired as well in spite of better asthma control scores.

Detecting litchis in a complex natural environment is important for yield estimation and provides reliable support to litchi-picking robots. This paper proposes an improved litchi detection model named YOLOv5-litchi for litchi detection in complex natural environments. First, we add a convolutional block attention module to each C3 module in the backbone of the network to enhance the ability of the network to extract important feature information. Second, we add a small-object detection layer to enable the model to locate smaller targets and enhance the detection performance of small targets. Third, the Mosaic-9 data augmentation in the network increases the diversity of datasets. Then, we accelerate the regression convergence process of the prediction box by replacing the target detection regression loss function with CIoU. Finally, we add weighted-boxes fusion to bring the prediction boxes closer to the target and reduce the missed detection. An experiment is carried out to verify the effectiveness of the improvement. The results of the study show that the mAP and recall of the YOLOv5-litchi model were improved by 12.9% and 15%, respectively, in comparison with those of the unimproved YOLOv5 network. The inference speed of the YOLOv5-litchi model to detect each picture is 25 ms, which is much better than that of Faster-RCNN and YOLOv4. Compared with the unimproved YOLOv5 network, the mAP of the YOLOv5-litchi model increased by 17.4% in the large visual scenes. The performance of the YOLOv5-litchi model for litchi detection is the best in five models. Therefore, YOLOv5-litchi achieved a good balance between speed, model size, and accuracy, which can meet the needs of litchi detection in agriculture and provides technical support for the yield estimation and litchi-picking robots.

It is necessary to develop automatic picking technology to improve the efficiency of litchi picking, and the accurate segmentation of litchi branches is the key that allows robots to complete the picking task. To solve the problem of inaccurate segmentation of litchi branches under natural conditions, this paper proposes a segmentation method for litchi branches based on the improved DeepLabv3+, which replaced the backbone network of DeepLabv3+ and used the Dilated Residual Networks as the backbone network to enhance the model’s feature extraction capability. During the training process, a combination of Cross-Entropy loss and the dice coefficient loss was used as the loss function to cause the model to pay more attention to the litchi branch area, which could alleviate the negative impact of the imbalance between the litchi branches and the background. In addition, the Coordinate Attention module is added to the atrous spatial pyramid pooling, and the channel and location information of the multi-scale semantic features acquired by the network are simultaneously considered. The experimental results show that the model’s mean intersection over union and mean pixel accuracy are 90.28% and 94.95%, respectively, and the frames per second (FPS) is 19.83. Compared with the classical DeepLabv3+ network, the model’s mean intersection over union and mean pixel accuracy are improved by 13.57% and 15.78%, respectively. This method can accurately segment litchi branches, which provides powerful technical support to help litchi-picking robots find branches.

Objective: Early identification of coronavirus disease 2019 (COVID-19) patients with worse outcomes may benefit clinical management of patients. We aimed to quantify pneumonia findings on CT at admission to predict progression to critical illness in COVID-19 patients.Methods: This retrospective study included laboratory-confirmed adult patients with COVID-19. All patients underwent a thin-section chest computed tomography (CT) scans showing evidence of pneumonia. CT images with severe moving artifacts were excluded from analysis. Patients' clinical and laboratory data were collected from medical records. Three quantitative CT features of pneumonia lesions were automatically calculated using a care.ai Intelligent Multi-disciplinary Imaging Diagnosis Platform Intelligent Evaluation System of Chest CT for COVID-19, denoting the percentage of pneumonia volume (PPV), ground-glass opacity volume (PGV), and consolidation volume (PCV). According to Chinese COVID-19 guidelines (trial version 7), patients were divided into noncritical and critical groups. Critical illness was defined as a composite of admission to the intensive care unit, respiratory failure requiring mechanical ventilation, shock, or death. The performance of PPV, PGV, and PCV in discrimination of critical illness was assessed. The correlations between PPV and laboratory variables were assessed by Pearson correlation analysis.Results: A total of 140 patients were included, with mean age of 58.6 years, and 85 (60.7%) were male. Thirty-two (22.9%) patients were critical. Using a cutoff value of 22.6%, the PPV had the highest performance in predicting critical illness, with an area under the curve of 0.868, sensitivity of 81.3%, and specificity of 80.6%. The PPV had moderately positive correlation with neutrophil (%) (r = 0.535, p < 0.001), erythrocyte sedimentation rate (r = 0.567, p < 0.001), d-Dimer (r = 0.444, p < 0.001), high-sensitivity C-reactive protein (r = 0.495, p < 0.001), aspartate aminotransferase (r = 0.410, p < 0.001), lactate dehydrogenase (r = 0.644, p < 0.001), and urea nitrogen (r = 0.439, p < 0.001), whereas the PPV had moderately negative correlation with lymphocyte (%) (r = −0.535, p < 0.001).Conclusions: Pneumonia volume quantified on initial CT can non-invasively predict the progression to critical illness in advance, which serve as a prognostic marker of COVID-19.

Abstract Background The differential sensitivity of cough to antitussive therapies implies the existence of heterogeneity in cough hypersensitivity, but how such heterogeneity is expressed across individual patients is poorly understood. We investigated the phenotypes of cough hypersensitivity by examining transient receptor potential ankyrin 1 (TRPA1)- and transient receptor potential vanilloid 1 (TRPV1)-mediated cough sensitivity in patients with chronic refractory cough. Methods Using a selective TRPA1 agonist, allyl isothiocyanate (AITC), we established an AITC cough challenge as a measure of TRPA1-mediated cough sensitivity. The AITC cough challenge and the widely used capsaicin (a selective TRPV1 agonist) cough challenge were performed with 250 patients with chronic refractory cough and 56 healthy subjects. The concentration of AITC or capsaicin solution causing at least two (C2) and five coughs (C5) was recorded. Cough sensitivity was expressed as the mean (95% confidence interval) of log C5, and cough hypersensitivity was defined as a log C5 value lower than that of healthy subjects. Results A distinct concentration–response effect of inhaled AITC was identified both in patients with chronic refractory cough and in healthy subjects. Cough sensitivity to AITC and capsaicin was significantly higher in patients than in healthy subjects (AITC: 2.42 [2.37–2.48] vs 2.72 [2.66–2.78] mM, p = 0.001; capsaicin: 1.87 [1.75–1.98] vs 2.53 [2.36–2.70] μM, p = 0.001) and was higher in females than in males for both healthy subjects and patients (all p

[This corrects the article DOI: 10.1371/journal.pone.0226400.].

Abstract The evolution of precipitates in P92 heat-resistant steel sustained under thermal aging at 923 K for different times were analyzed by scanning electron microscopy (SEM) and transmission electron microscopy (TEM), while the effects of precipitates evolution on the low stress creep properties were experimentally analyzed through uniaxial creep testing and multiaxial “helicoid spring creep testing”. The results demonstrate that the coarsening of M23C6 carbide is significantly slower than the Laves phase in the thermal aging of 0~8000 h. The creep resistance of the P92 heat-resistant steel is enhanced at 923 K aging for 3000 h, and the strengthening effect is significantly apparent under the lower stresses. Moreover, the microstructure degradation factor of the P92 heat-resistant steel in the low stress region at 923 K is different that under the lower stresses of 20 MPa and 35 MPa is mainly Laves phase and the higher stresses of 65 MPa and 75 MPa is mainly M23C6 carbide.

The determination of crop water status has positive effects on the Chinese Brassica industry and irrigation decisions. Drought can decrease the production of Chinese Brassica, whereas over-irrigation can waste water. It is desirable to schedule irrigation when the crop suffers from water stress. In this study, a random forest model was developed using sample data derived from meteorological measurements including air temperature (Ta), relative humidity (RH), wind speed (WS), and photosynthetic active radiation (Par) to predict the lower baseline (Twet) and upper baseline (Tdry) canopy temperatures for Chinese Brassica from 27 November to 31 December 2020 (E1) and from 25 May to 20 June 2021 (E2). Crop water stress index (CWSI) values were determined based on the predicted canopy temperature and used to assess the crop water status. The study demonstrated the viability of using a random forest model to forecast Twet and Tdry. The coefficients of determination (R2) in E1 were 0.90 and 0.88 for development and 0.80 and 0.77 for validation, respectively. The R2 values in E2 were 0.91 and 0.89 for development and 0.83 and 0.80 for validation, respectively. Our results reveal that the measured and predicted CWSI values had similar R2 values related to stomatal conductance (~0.5 in E1, ~0.6 in E2), whereas the CWSI showed a poor correlation with transpiration rate (~0.25 in E1, ~0.2 in E2). Finally, the methodology used to calculate the daily CWSI for Chinese Brassica in this study showed that both Twet and Tdry, which require frequent measuring and design experiment due to the trial site and condition changes, have the potential to simulate environmental parameters and can therefore be applied to conveniently calculate the CWSI.

Water stress is a significant element impacting photosynthesis, which is one of the major physiological activities governing crop growth and development. In this study, the photosynthetic rate of Brassica chinensis L. var. parachinensis (Bailey) (referred to as Chinese Brassica hereafter) was predicted using the deep learning method. Five sets of Chinese Brassica were created, each with a different water stress gradient. Air temperature (Ta), relative humidity (RH), canopy temperature (Tc), transpiration rate (Tr), photosynthetic rate (Pn), and photosynthetically available radiation (PAR) were measured in different growth stages. The upper limit and lower limit equations were built using the non-water-stress baseline (NWSB) and hierarchical density-based spatial clustering of applications with noise (HDBSCAN) methods. The crop water stress index (CWSI) was then calculated using these built equations. The multivariate long short-term memory (MLSTM) model was proposed to predict Pn based on CWSI and other parameters. At the same time, the support vector regression (SVR) method was applied to provide a comparison to the MSLTM model. The results show that water stress had an important effect on the growth of Chinese Brassica. The more serious the water stress, the lower the growth range (GR). The HDBSCAN method had a lower root mean square error (RMSE) in calculating CWSI. Furthermore, the CWSI had a significant effect on predicting Pn. The regression fitting between measured Pn and predicted Pn showed that the determination coefficient (R2) and RMSE were 0.899 and 0.108 μmol·m−2·s−1, respectively. In this study, we successfully developed a method for the reliable prediction of Pn in Chinese Brassica, which can serve as a useful reference for application in water saving.

In order to create an irrigation scheduling plan for use in large-area citrus orchards, an environmental information collection system of citrus orchards was established based on the Internet of Things (IoT). With the environmental information data, deep bidirectional long short-term memory (Bid-LSTM) networks are proposed to improve soil moisture (SM) and soil electrical conductivity (SEC) predictions, providing a meaningful reference for the irrigation and fertilization of citrus orchards. The IoT system contains SM, SEC, air temperature and humidity, wind speed, and precipitation sensors, while the mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2) were calculated to evaluate the performance of the models. The performance of the deep Bid-LSTM model was compared with a multi-layer neural network (MLNN). The results for the performance criteria reveal that the proposed deep Bid-LSTM networks perform better than the MLNN model, according to many of the evaluation indicators of this study.

The aim of our study was to elucidate the landscapes of genetic alterations of TSC1 and TSC2 as well as other possible non-TSC1/2 in Lymphangioleiomyomatosis (LAM) patients. Sixty-one Chinese LAM patients' clinical information was collected. Tumor biopsies and matched leukocytes from these patients were retrospectively analyzed by next generation sequencing (NGS), chromosomal microarray analysis (CMA), and multiplex ligation-dependent probe amplification (MLPA). Eighty-six TSC1/2 variants were identified in 46 of the 61 LAM patients (75.4%) in which TSC2 and TSC1 variants were 88.37% and 11.63% respectively. The 86 variants are composed of (i) 52 single nucleotide variants (SNVs) (including 30 novel variants), (ii) 23 indels (including 21deletions, and 2 insertions), (iii) a germline duplication of exon 31-42 of TSC2, (iv) a 2.68 Mb somatic duplication containing TSC2, and (v) 9 regions with copy-neutral loss of heterogeneity (CN-LOHs) present only in the LAM patients with single TSC1/2 mutations. Sixty-one non-TSC1/2 variants in 31 genes were identified in 37 LAM patients. Combined applications of different techniques are necessary to achieve maximal detection rate of TSC1/2 variants in LAM patients. Thirty novel TSC1/2 variants expands the spectrum of TSC1/2 in LAM patients. Identification of 61 non-TSC1/2 variants suggests that alternative genes might have contributed to the initiation and progression of LAM.